Our study evaluated the effects of vineyard abandonment and shrub encroachment and the value of active vineyard management for Orthoptera diversity. We investigated orthopterans in two different vineyard management types  and in vineyard fallows in the Upper Middle Rhine Valley (UMRV) in Germany and determined the effects of local vegetation structures within vineyards and in the surrounding landscape. On the landscape and local scale, woody structures and vineyard abandonment reduced habitat quality, especially for open-adapted Orthoptera species. Management in vineyard inter-rows provided bare ground and supported heat- and open-adapted species while taller vegetation stands on terrace embankments enhanced Orthoptera species richness. We conclude that maintaining viticulture on steep slopes is significant for the preservation of open and herbaceous habitat structures and the conservation of associated Orthoptera species.The abandonment of traditional agricultural practices and subsequent succession are major threats to many open-adapted species and species-rich ecosystems. Viticulture on steep slopes has recently suffered from strong declines due to insufficient profitability, thus increasing the area of fallow land considerably. Changing cultivation systems from vertically oriented to modern vineyard terraces offers an opportunity to maintain management economically viable and thus reduces further abandonment. Hillside parallel terraces favor mechanization, and their embankments offer large undisturbed areas that could provide valuable habitats. We investigated the effects of vineyard abandonment, different vineyard management types , and local parameters on Orthoptera diversity in 45 study sites along the Upper Middle Rhine Valley in Germany. Our results show that woody structures and vineyard abandonment reduced Orthoptera diversity at the local and landscape scale due to decreased habitat quality, especially for open-adapted species. In contrast, open inter-rows of actively managed vineyard types supported heat-adapted Caelifera species. On terrace embankments, extensive management and taller vegetation benefited Ensifera species, while short and mulched vegetation in vertically oriented vineyards favored the dominance of one single Caelifera species. Our results highlight the significance of maintaining viticultural management on steep slopes for the preservation of both open-adapted Orthoptera species and the cultural landscape. Insect populations are declining at unprecedented rates ,2,3. TheLacerta bilineata), Red-winged grasshopper (Oedipoda germanica), and Scarce swallowtail  [In Germany, viticulture on steep slopes has a long tradition and has shaped entire cultural landscapes such as the Mosel or the Upper Middle Rhine Valley. Traditionally, vines were cultivated on small terraces made of dry-stone walls intermingled with natural elements such as hedges, trees, and clearance cairns . This tralirius) ,11. Durialirius) . Howeveralirius) . Consequalirius) . Of the alirius) . The proOne approach to halting viticultural decline on steep slopes is modern vineyard terracing see B. Here, Orthopterans are important components of grassland invertebrate assemblages in European agricultural ecosystems , particuThis study aims to assess the value of steep slope viticulture for Orthoptera diversity and to evaluate the impacts of ongoing succession as a consequence of vineyard abandonment. In this study, we compared Orthoptera diversity among abandoned vineyards  and two different vineyard management types  in a viticultural landscape in Germany and addressed the following research questions: (i) How do Orthoptera species richness, density, and species composition shift across abandoned and managed vineyard types? (ii) How do management-related structures within vineyards affect species richness, density, and species composition? (i)vertically oriented vineyards received an alternating tillage treatment, i.e., every second inter-row was kept open by regular tillage while the other inter-row was permanently covered with grassy vegetation dominated by Lolium perenne. In order to prevent competition for water and nutrients with vines, inter-row vegetation was kept short by regular mulching.(ii)terraced vineyards received a regular tillage treatment in inter-rows, while terrace embankments were permanently covered with grassy and herbaceous vegetation such as Arrhenaterum elatius, Bromus erectus, Galium album, and Isatis tinctoria. Embankment vegetation was extensively managed and mulched/mown once in summer.(iii)vineyard fallows were abandoned for at least 10 years and were mostly overgrown with woody vegetation dominated by Rubus fruticosus agg., Rosa canina agg., Prunus avium, and Crataegus laevigata.The study area (75–80 m a.s.l.) is situated in the winegrowing region of the Upper Middle Rhine Valley (UMRV) in Rhineland-Palatinate  and Hesse , Germany . The cliThe vineyard types were arranged in triplets, with each triplet including a vertically oriented vineyard, a terraced vineyard, and a vineyard fallow . We studOrthoptera sampling was carried out from late July to mid-August 2020 during sunny and warm weather conditions using suction sampling, according to Mody et al. 2020 . To vacu2 per site. To account for structural differences within vineyards, we sampled vegetated and open inter-rows in vertically oriented vineyards and inter-rows and embankments in terraced vineyards (hereafter vineyard compartments) separately. For each of the two vineyard compartments per managed vineyard, we sampled 8 m2. Sampling plots (1 m2) were spaced at a five-meter distance and split up into two neighboring vineyard compartments of the same type in the center of each site. In managed vineyards , we vacuumed a total area of 16 m2 were randomly selected in the center and vacuumed for one minute each. In addition, we also sampled the vine canopy at four randomly picked managed vineyard pairs by constantly vacuuming both sites of the canopy for one minute at two different positions each. Due to great structural differences in vineyard fallows (dense shrubby vegetation) compared to managed vineyards, Orthoptera sampling had to be conducted differently. For vineyard fallows, two sampling locations of about 2 m2 and frozen until further processing [Orthoptera were directly determined after sampling using the determination keys of Fischer et al. . Speciesocessing ,29. JuveIn parallel with Orthoptera sampling, local vegetation parameters were recorded in two square plots of 1 × 1 m for every vineyard compartment and vineyard fallow. Within each plot, the percentage cover of bare ground, litter, vegetation, and shrubs was visually estimated. In this study, we defined litter as loose dead plant material that frequently occurred in high percentages due to dry and hot weather conditions and vegetation management (mulching). In addition, the average vegetation and shrub height was measured (two random measurements). For data analyses, mean values were calculated.As landscape context might be a potential determinant of orthopterans , the cov2 for each vineyard type and vineyard compartment and assigned Orthoptera to sub-orders (Caelifera and Ensifera). To analyze differences between vineyard types for Orthoptera species richness, density, and local vegetation parameters,  linear mixed effect models (GLMM) were fitted with vineyard type and vineyard compartment as a fixed effect and ‘vineyard triplet’ (factor with 15 levels) as a random effect to account for the nested design . DependglmmTMB; . For vinp-values were obtained using a permutation test with 9999 permutations.To identify species associated with vineyard types and compartments, we performed an indicator species analysis using the IndVal procedure of Dufrêne and Legendre . This anp < 0.05, based on a permutation test with 9999 permutations) were included post-hoc by projection. Before the analysis, all local vegetation and landscape variables were standardized to zero mean and unit variance . The community data were log10 (x + 1) transformed to reduce the influence of dominant species. Additionally, species with less than two occurrences were excluded from the analysis, thus reducing the number of species from 15 to 7. To unravel the influence of different vineyard types and compartments on Orthoptera assemblages, indirect ordination methods were applied using the vegan package . To starAll analyzes were conducted in R version 3.6.3 . FiguresChorthippus biguttulus  was the most abundant species , followed by Oedipoda caerulescens  with 32 individuals . Five species were only sampled once , Chorthippus dorsatus , Nemobius sylvestris , Phaneroptera nana , Pholidoptera griseoaptera ). We collected three species of conservation concern, which are listed on the Red List of Orthoptera in Germany [L. punctatissima (1), O. pellucens (2)) and therefore did not consider them for further analysis. In total, we sampled 254 Orthoptera individuals and 15 species . Thereof Germany   but between managed vineyards and fallows . VegetatWithin managed vineyard types, local vegetation parameters differed significantly between compartments of the respective vineyard type . In vertOrthoptera species richness differed significantly between vineyard types, with terraced vineyards having the highest and fallows having the lowest species richness A. VerticC. biguttulus dominated the community in vertically oriented vineyards and was the only indicator species assigned to this vineyard type  and one belonging to the Caelifera-group (O. caerulescens) (ard type . For terlescens) . There wWithin vertically oriented vineyards, neither species richness nor density was significantly different between vegetated and open inter-rows A,B. For C. biguttulus and O. caerulescens in vineyard types and vineyard compartments  had a positive effect on Orthoptera and Ensifera species richness, while Ensifera density was negatively affected by a cover of SNH but positively affected by vineyard cover in the surrounding landscape  appeared to be lower than in other habitats such as grassland (3.5 to 7.4/m2) [2) [2) [The orthopteran species we recorded in our study represent approximately 20% of the German Orthoptera fauna . The spe 7.4/m2) ,44, fiel [2) [2) . O. germanica that colonizes hot and dry vegetation-free and rocky habitats and is sensitive to denser vegetation, ongoing succession strongly reduces habitat quality [In grasslands and heathlands, Orthoptera associated with open habitats are negatively affected by older successional stages overgrown with woody vegetation resulting in lower species richness ,25,45. I quality . On the  quality  and are  quality . However quality . As foreSampling orthopterans in taller vegetation (>50 cm sward height) can be more problematic, and the efficiency of sampling in fallows might be reduced by woody vegetation structures . HoweverP. falcata but were still positively correlated with a greater distance to SNHs  and cover of vineyards in the surrounding landscape since habitat conditions on vineyard fallows were less suitable , typically found on south-exposed embankments and dry shrubbery grasslands, likely profited from high insolation on embankments combined with tall vegetation and a small proportion of shrubs on embankments [Even though most Orthoptera species are polyphagous and do not depend on specific host plants for their survival, plant community composition is widely considered a determining factor for Orthoptera diversity ,57. A diable see . The higr adults ,61. In aankments . The relatively high litter cover in our study was the result of withered vegetation due to heat and drought and residuals from mulching in vegetated inter-rows and embankments. Although litter is usually associated with unfavorable habitat conditions for Orthoptera, Gaigher and Samways 2010 , found tC. biguttulus, a grass-feeding generalist that colonized a broad range of habitats and seemed to be less sensitive to mulching management and short vegetation in vegetated inter-rows. These results coincide with findings from South African vineyards where Orthoptera diversity was low while Orthoptera density in inter-rows  was high and mostly attributed to the dominance of one single species [O. caerulescens, which is known for its preference for sparsely vegetated, dry, and sun-exposed soils for basking [O. caerulescens depends on a certain amount of herbaceous vegetation for oviposition [O. germanica and C. vargans on sites with slate soils that heat up stronger but are even more affected by abandonment and subsequent natural forest recovery as the wine growing at these sites is more effortful and less profitable.The high density of Orthoptera and Caelifera in vertically oriented vineyards was driven by the dominance of  species . Caelife species ,64. Most basking . Neverthposition  providedLobesia botrana, one of the major grapevine pests in Europe [Meconema meridionale) are known to prey on pests such as leaf miners and chrysomelids [To summarize, Orthoptera diversity in vineyards was supported by habitat heterogeneity created by alternating management within vineyards that provided different microhabitats at a site such as bare ground, short and sparse, and taller vegetation and thus met the diverse requirements many orthopterans have during different life stages . Low-intn Europe  and othesomelids ,69.Iphiclides podalirius). Faced with the challenges of cost disadvantages in steep slope viticulture, vineyard terracing provides a viable solution to maintaining wine growing economically sustainable and, at the same time, creating valuable habitats within the viticultural system. In particular, the extensive management and low degree of disturbance of the terrace embankments met the distinct habitat requirements that orthopterans have in different life stages by providing both open bare ground areas and vegetative cover.Orthopteran diversity strongly decreased in vineyard fallows, and all open-adapted species disappeared. Therefore, maintaining viticulture on steep slopes to preserve open structures is of great importance both on a local and landscape scale for the conservation of endangered orthopterans, but also for the conservation of habitats for other endangered species such as the Scarce swallowtail (


Furthermore, enzymatic assay was performed against EGFR and HER2 to prove the dual inhibitory action. Compounds 6e and 6k showed potent inhibitory activity for EGFR with IC50 of 0.009 and 0.051 µM, respectively, and for HER2 with IC50 of 0.013 and 0.027 µM, respectively. Additionally, compounds 6e and 6k significantly stimulated apoptotic breast cancer cell death. Compound 6e was further explored for its anticancer activity in vivo using a Xenograft model. Moreover, computational modeling studies, ADMET studies and toxicity prediction were performed to investigate their potential drug candidates.Breast cancer is a disease in which cells in the breast divide continuously without control. There are great limitations in cancer chemotherapy. Hence, it is essential to search for new cancer therapeutics. Herein, a novel series of EGFR/HER2 dual inhibitors has been designed based on the hybridization of thiazole and pyrazoline fragments. The synthesized compounds were screened for their anti-proliferative activity against MCF-7 breast cancer cell line and MCF-10 normal breast cell line. Interestingly, synthesized compounds  Cancer is considered one of the most challenging causes of death in the world, as it is estimated to have caused nearly 10 million deaths in 2020 according to WHO . The traProtein kinases represent the fifth largest human protein family that exert their functions as key regulators of signal transduction mechanisms such as proliferation, metabolism, vascularization, and apoptosis ,11,12,1350 = 0.13 µM, IC50 = 0.42 µM and IC50 = 34.10 µM, respectively .,58.3a–c,3a–c with an equimolar amount of phenacyl bromide derivatives 5a–d . Co. Co3a–c 8a–b were synthesized by treating pentane-2,4-dione or methyl 3-oxobutanoate with sulfuryl chloride (SO2Cl2) . IR. IR8a–b 9d–f displayed a band at 1708–1695 cm−1 representing C=O. 1H NMR spectrum also showed a typical quartet–triplet pattern of the ethyl protons at δ 4.2 and 1.25 ppm, respectively. However, signals of thiazole CH3 appeared at δ 2.36–2.35 ppm. Furthermore, 13C NMR spectrum revealed the signals of ethyl carbons at δ 14.8 and 60.6 ppm, and signal at 165.1–165.2 ppm attributed C=O of ester.IR spectra of compounds 3c, 6b and 6f were found to be more cytotoxic against MCF-7, with IC50 ranges of 11.09, 13.36 and 11.05 µM, respectively. Interestingly, compounds 6e, 9f and 6k exhibited remarkable cytotoxic activities, with IC50 values of 7.21, 8.35 and 8.02 µM, respectively, compared to Lapatinib (IC50 = 7.45 µM) with non-cytotoxic activity against the MCF-10A. Other compounds exhibited relatively moderate-to-weak cytotoxic activities. Hence, three compounds; 6e, 9f, and 6k, were chosen to be studied for their potential molecular targets and ability to induce apoptosis in MCF-7 cells . As seen in 6k, and 6e significantly stimulated apoptotic breast cancer cell death with 22.44%  and 27.99% , respectively, compared to 0.68% in the control.Flow cytometric analysis of Annexin V/PI staining was used to examine apoptotic cell death in untreated and treated MCF-7 cells to determine the apoptotic activity of the two promising compounds, 6e, indicating that compound 6e induced cell cycle arrest in the S phase, while the percentage of cells in the G2/M phase was reduced by 2.01% after treatment with compound 6k, compared to the control (9.7%). This result indicates that compound 6k induced cell cycle arrest in the G2/M-phase. By comparison, other phases were not significantly changed in either treatment.Cytotoxic substances were tested using DNA flow cytometry to determine what percentage of cells were in each cell cycle following treatment. 6k and 6e in MCF-7 cells, the gene expression levels of apoptosis-related genes in both untreated and treated MCF-7 cells through the RT-PCR. As seen in 6k and 6e treatments increased P53 level by 3.91- and 4.8-fold, Bax level by 5.24- and 6.39-fold, caspase 3 level by 5.79- and 7.12-fold, caspase 8 level by 3.91- and 1.71-fold, caspase 9 level by 6.07- and 8.19-fold, while both compound treatments decreased Bcl-2 level “as the anti-apoptotic gene” by 0.41- and 0.32-fold compared to untreated control.Further validation of the apoptosis-inducing activities of both tested compounds P53 is a tumor suppressor gene that is essential for apoptosis. The activation of caspases 3 and 9 and the decreased expression of the Bcl-2 gene may cause p53 apoptosis. The results showed the intrinsic apoptotic pathway through activation of P53, Bax, and caspases 3 and 9 levels. Further, the treatment-induced upregulation of caspase 8 gene promotes an extrinsic apoptotic pathway in MCF-7 cells. As a result, our RT-PCR findings corroborated those of previous studies, showing that the apoptotic pathway is highlighted by the upregulation of proapoptotic genes and the downregulation of anti-apoptotic genes .6e, mice, MCF-7 cells (Xenograft model) were treated with compounds 6e and lapatinib at a dose . As seen in 6e and lapatinib reduced the mice weight to 28.23 g and 27.35 g, respectively. The reduction in the mice’s weight was consequently related to a decrease in the tumor mass; tumor volume decreased from 89.8 mg to 28.4 mm3, respectively, upon treatment with compound 6e. Hence, compound 6e treatment enhanced tumor inhibition ratio by 52.46% compared to 50.53% in Lapatinib treatment For evaluation of the anticancer activity of compound 6/L), respectively. Conversely, white blood cell count was elevated to 7.0 (103/L) from normal control levels. Routine consequences of tumor proliferation are reduced levels of RBC, hemoglobin, and increased WBC counts −; Anal. Calcd. For: C19H21N3O3S (371.5): C, 61.44; H, 5.7; N, 11.31; S, 8.63; Found: C, 61.72; H, 5.86; N, 11.58; S, 8.75.White powder, 80% yield; m.p. 221–223 °C; IR (KBr) −1 3393, 3265 (NH2), 3010 , 2936 , 1604 ( C=N), 1572 (C=C); 1H NMR (DMSO-d6) δ 8.04 , 7.64 , 7.37 , 7.25 , 7.16  6.98 , 5.92 , 3.75 , 3.84 , 3.79 , 3.14 ; 13C NMR (DMSO-d6) δ 42.73 (C4 pyrazoline), 56.07 (4-OCH3), 56.14 (3-OCH3), 62.72 (C5 pyrazoline), 109.93 (Ar-C), 111.65 (Ar-C), 121.65 (Ar-C), 123.81 (Ar-C), 127.79 (2x C Ar), 128.92 (2x C Ar), 131.86 (Ar-C), 142.51 (Ar-C), 149.33 (Ar-C), 151.62 (Ar-C), 155.37 (Ar-C), 176.17 (CS). MS (APCI+) m/z (%) 376.5 [M + 1]+; Anal. Calcd. For: C18H18N3O2SCl (375.87): C, 57.52; H, 4.83; N, 11.18; S, 8.53; Found: C, 57.65; H, 4.97; N, 11.45; S, 8.70.Yellowish white powder, 60% yield; m.p. 230–232 °C; IR (KBr) νmax/cm5a–d (0.6 mmol) was added to a solution of pyrazoline derivatives 3a–c (0.6 mmol) in absolute ethanol (15 mL), before it was refluxed at 90 °C for 5 h. After cooling, the obtained solid was filtered off, washed with ethanol, and recrystallized from EtOH/DMF to give compounds 6a–l.The appropriate amount of phenacyl bromide −1 3108 , 2959 , 1600 (C=N), 1543, 1543 (Ar C=C); 1H NMR (DMSO-d6) δ 7.71 , 7.43–7.23 , 7.05 , 5.67 , 4.05 , 3.84 , 3.82 , 3.36 ; 13C NMR (DMSO-d6) δ 43.87 (C4 pyrazoline), 56.02 (4-OCH3), 56.1 (3-OCH3), 64.56 (C5 pyrazoline), 104.53 (Ar-C), 109.51 (Ar-C), 112.10 (Ar-C), 120.63 (Ar-C), 124.06 (Ar-C), 125.93 (2x C Ar), 127 (2x C Ar), 127.95 (2x C Ar), 128.95 (2x C Ar), 129.03 (2x C Ar), 134.96 (Ar-C), 142.49 (Ar-C), 149.29 (Ar-C), 150.87 (Ar-C), 151.09 (Ar-C), 153.39 (Ar-C), 164.90 (Ar-C). MS (ESI+) m/z (%) 442.5[M + H]+; Anal. Calcd. For: C26H23N3O2S (441.55): C, 70.73; H, 5.25; N, 9.52; S, 7.26; Found: C, 70.95; H, 5.41; N, 9.80; S, 7.43.Yellowish white powder, 55% yield; m.p. 202–205 °C; IR (KBr) νmax/cm−1 2923 , 1599 (C=N), 1549, 1500 (C=C). 1H NMR (DMSO-d6) δ 7.61 , 7.43–7.25 , 7.21 , 7.14 , 7.03 , 5.64 , 4.03 , 3.84 , 3.81 , 3.30 , 2.28 ; 13C NMR (DMSO-d6) δ 21.24 (4-CH3), 43.81 (C4 pyrazoline), 55.99 (4-OCH3), 56.07 (3-OCH3), 64.57 (C5 pyrazoline), 103.59 (Ar-C), 109.47 (Ar-C), 112.04 (Ar-C), 120.59 (Ar-C), 124.08 (Ar-C), 125.90 (2 x C Ar), 127.02 (2 x C Ar), 127.91 (Ar-C), 128.99 (2 x C Ar), 129.50 (2 x C Ar), 132.36 (Ar-C), 137.21 (Ar-C), 142.50 (Ar-C), 149.29 (Ar-C), 150.98 (Ar-C), 151.08 (Ar-C), 153.24 (Ar-C), 164.84 (Ar-C); MS m/z (%) 455.27 (M+); Anal. Calcd. For: C27H25N3O2S (455.58): C, 71.18; H, 5.53; N, 9.22; S, 7.04; Found: C, 71.42; H, 5.67; N, 9.49; S, 7.18.White powder, 77.6% yield; m.p. 210–215 °C; IR (KBr) νmax/cm−1 1601(C=N), 1551, 1501(C=C); 1H NMR (DMSO-d6) δ 7.75 , 7.41–7.25 , 7.18 , 7.04 , 5.65 , 4.04 , 3.84 , 3.82 , 3.30 ; 13C NMR (DMSO-d6) δ 43.89 (C4 pyrazoline), 56.02 (4-OCH3), 56.10 (3-OCH3), 64.54 (C5 pyrazoline), 104.26 (Ar-C), 109.52 (Ar-C), 112.08 (Ar-C), 115.68 (Ar-C), 115.89 (Ar-C), 120.64 (Ar-C), 124.03 (Ar-C), 126.98 (2 x C Ar), 127.85 (Ar-C), 127.94 (Ar-C), 129.04 (2 x C Ar), 131.61 (Ar-C), 142.45 (Ar-C), 149.29 (Ar-C), 149.83 (Ar-C), 151.11 (Ar-C), 153.47 (Ar-C), 160.80 (Ar-C), 163.23 (Ar-C), 164.99 (Ar-C). MS (ESI+) m/z (%) 460.2 [M + 1]+; Anal. Calcd. For: C26H22N3O2SF (459.54): C, 67.96; H, 4.83; N, 9.14; S, 6.98; Found: C, 67.78; H, 5.01; N, 9.42; S, 6.85.White powder, 76% yield; m.p. 135–140 °C; IR (KBr) νmax/cm−1 2935 , 1600 (C=N), 1549 (C=C); 1H NMR (DMSO-d6) δ 7.72 , 7.41–7.25 , 7.04 , 5.66 , 4.04 , 3.84 , 3.82 , 3.35 ; 13C NMR (DMSO-d6) δ 43.43 (C4 pyrazoline), 55.55 (-OCH3), 55.64 (-OCH3), 64.03 (C5 pyrazoline), 104.88 (Ar-C), 109.07 (Ar-C), 111.61 (Ar-C), 120.19 (Ar-C), 123.54 (Ar-C), 126.52(2 x C Ar), 127.15(2 x C Ar), 127.49 (Ar-C), 128.51(2 x C Ar), 128.59 (2 x C Ar), 131.88 (Ar-C), 133.37 (Ar-C), 141.93 (Ar-C), 148.83 (Ar-C), 149.17 (Ar-C), 150.67 (Ar-C), 153.11 (Ar-C), 164.53 (Ar-C). MS (APCI+) m/z (%) 476.6 (M + 1)+; Anal. Calcd. For: C26H22N3O2SCl (476): C, 65.61; H, 4.66; N, 8.83; S, 6.74; Found: C, 65.87; H, 4.80; N, 9.09; S, 6.85.White powder, 81.3% yield; m.p. 195–198 °C; IR (KBr) νmax/cm−1 1603, 1535; 1H NMR (DMSO-d6) δ 7.75 , 7.39–7.24 , 7.05 , 6.93 , 5.62 , 4.00 , 3.85 , 3.83 , 3.72 , 3.34 ; 13C NMR (DMSO-d6) δ 43.70 (C4 pyrazoline), 55.51 (4-OCH3), 56.01 (4-OCH3), 56.09 (3-OCH3), 64.03 (C5 pyrazoline), 104.41 (Ar-C), 109.48 (Ar-C), 112.07 (Ar-C), 114.34 (2 x C Ar), 120.59 (Ar-C), 124.16 (Ar-C), 125.97 (2 x C Ar), 127.94 (Ar-C), 128.37 (2 x C Ar), 128.96 (2 x C Ar), 134.38 (Ar-C), 135.03 (Ar-C), 149.31 (Ar-C), 150.91 (Ar-C), 151.08 (Ar-C), 153.32 (Ar-C), 159.06 (Ar-C), 164.87 (Ar-C). MS m/z (%) 471.45 (M+); Anal. Calcd. For: C27H25N3O3S (471.58): C, 68.77; H, 5.34; N, 8.91; S, 6.80; Found: C, 69.04; H, 5.60; N, 9.17; S, 6.93.Off-white powder, 36.6% yield; m.p. 168–170 °C; IR (KBr) νmax/cm−1 2932 , 1605, 1549; 1H NMR (DMSO-d6) δ 7.64 , 7.37–7.28 , 7.2 , 7.16 , 7.03 , 6.93 , 5.6 , 3.96 , 3.84 , 3.82 , 3.71 , 3.34 , 2.296 ; 13C NMR (DMSO-d6) δ 21.25 (CH3), 43.65 (C4 pyrazoline), 55.51 (4-OCH3), 56.00 (4-OCH3), 56.08 (3-OCH3), 64.02 (C5 pyrazoline), 103.47 (Ar-C), 109.45 (Ar-C), 112.06 (Ar-C), 114.31 (2 x C Ar), 120.57 (Ar-C), 124.17 (Ar-C), 125.92 (2 x C Ar), 128.39 (2 x C Ar), 129.52 (2 x C Ar), 132.39 (Ar-C), 134.38 (Ar-C), 137.21 (Ar-C), 149.30 (Ar-C), 150.98 (Ar-C), 151.06 (Ar-C), 153.23 (Ar-C), 159.05 (Ar-C), 164.79 (Ar-C). MS (ESI+) m/z (%) 486.7 [M + H]+; Anal. Calcd. For: C28H27N3O3S (485.6): C, 69.26; H, 5.6; N, 8.65; S, 6.6; Found: C, 69.47; H, 5.78; N, 8.81; S, 6.72.Brown powder, 45% yield; m.p. 150–152 °C; IR (KBr) νmax/cm−1 1602, 1550; 1H NMR (DMSO-d6) δ 7.79 , 7.38 , 7.33 , 7.29 , 7.25 , 7.19 , 7.02 , 6.91 , 5.6 , 3.96 , 3.84 , 3.81 , 3.71 , 3.34 ; 13C NMR (DMSO-d6) δ 43.69 (C4 pyrazoline), 55.47 (4-OCH3), 55.97 (3-OCH3), 56.04 (4-OCH3), 64.04 (C5 pyrazoline), 104.11 (Ar-C), 109.46 (Ar-C), 112 (Ar-C), 114.33 (2 x C Ar), 115.67 (Ar-C), 115.89 (Ar-C), 120.59 (Ar-C), 124.15 (Ar-C), 127.88 (Ar-C), 127.96 (Ar-C), 128.36 (2 x C Ar), 131.66 (Ar-C), 131.69 (Ar-C), 134.32 (Ar-C), 149.32 (Ar-C), 149.9 (Ar-C), 151.11 (Ar-C), 153.36 (Ar-C), 159.07 (Ar-C), 160.83 (Ar-C), 163.26 (Ar-C), 164.97 (Ar-C). MS (APCI+) m/z (%) 490.63 [M + H]+; Anal. Calcd. For: C27H24N3O3SF (489.57): C, 66.24; H, 4.94; N, 8.58; S, 6.55; Found: C, 66.51; H, 5.11; N, 8.75; S, 6.68.Brown powder, 58% yield; m.p. 140–143 °C; IR (KBr) νmax/cmνmax/cm−1 1608 (C=N), 1552 (C=C); 1H NMR (DMSO-d6) δ 7.76 , 7.42  7.37–7.28 , 7.05 , 6.92 , 5.62 , 4.00 , 3.85 , 3.83 , 3.72 , 3.35 ; 13C NMR (DMSO-d6) δ 43.70 (C4 pyrazoline), 55.52 (-OCH3), 56.01 (-OCH3), 56.09 (-OCH3), 63.98 (C5 pyrazoline), 105.21 (Ar-C), 109.49 (Ar-C), 112.07 (Ar-C), 114.35 (2 x C Ar), 120.63 (Ar-C), 124.09 (Ar-C), 127.64 (2 x C Ar), 128.38 (2 x C Ar), 128.99 (2 x C Ar), 132.34 (Ar-C), 133.87 (Ar-C), 134.24 (Ar-C), 149.30 (Ar-C), 149.66 (Ar-C), 151.11 (Ar-C), 153.53 (Ar-C), 159.07 (Ar-C), 164.95 (Ar-C). MS (ESI-) m/z (%) 505.2 [M-H]-, 508.5 [M + 2]+; Anal. Calcd. For: C27H24N3O3SCl (506): C, 64.09; H, 4.78; N, 8.3; S, 6.34; Found: C, 64.23; H, 4.93; N, 8.47; S, 6.50.White powder, 55% yield; m.p. 149–151 °C; IR (KBr) −1 1599 (C = N), 1544 (C = C); 1H NMR (DMSO-d6) δ 7.72 , 7.44–7.46 , 7.38–7.24 , 7.06 , 5.67 , 4.04 , 3.85 , 3.83 , 3.37 ; 13C NMR (DMSO-d6) δ 43.67 (C4 pyrazoline), 56.01 (4-OCH3), 56.09 (3-OCH3), 63.97 (C5 pyrazoline), 104.68 (Ar-C), 109.52 (Ar-C), 112.06 (Ar-C), 120.68 (Ar-C), 123.95 (Ar-C), 125.93 (Ar-C), 128 (3 x C Ar), 129.(4 x C Ar), 129.03 (Ar-C), 132.45 (Ar-C), 134.91 (Ar-C), 141.42 (Ar-C), 149.29 (Ar-C), 150.88 (Ar-C), 151.15 (Ar-C), 153.45 (Ar-C), 164.90 (Ar-C); MS (APCI+) m/z (%) 476.4 [M+], 478.4 [M + 2]+; Anal. Calcd. For: C26H22N3O2S CL (476): C, 65.61; H, 4.66; N, 8.83; S, 6.74; Found: C, 65.43; H, 4.85; N, 9.12; S, 6.78.Brown powder, 66% yield; m.p. 208–210 °C; IR (KBr) νmax/cm−1 1601 (C=N), 1547 (C=C); 1H NMR (DMSO-d6) δ 7.60 , 7.44–7.46  7.36 , 7.31 , 7.23 , 7.16 , 7.05 , 5.67 , 4.03 , 3.84 , 3.82 , 3.34 , 2.29 ; 13C NMR (DMSO-d6) δ 21.26 (CH3), 43.65 (C4 pyrazoline), 56.02 (4-OCH3), 56.1 (3-OCH3), 63.97 (C5 pyrazoline), 103.75 (Ar-C), 109.54 (Ar-C), 112.08 (Ar-C), 120.65 (Ar-C), 123.99 (Ar-C), 125.88 (2 x C Ar), 128.98 (2 x C Ar), 129.05 (2 x C Ar), 129.54 (2 x C Ar), 132.29 (Ar-C), 132.42 (Ar-C), 137.25 (Ar-C), 141.46 (Ar-C), 149.30 (Ar-C), 150.93 (Ar-C), 151.14 (Ar-C), 153.37 (Ar-C), 164.80 (Ar-C). MS m/z (%) 491.4 [M + H]+; Anal. Calcd. For: C27H24N3O2S Cl (490): C,66.18; H, 4.94; N, 8.58; S, 6.54; Found: C, 66.46; H, 5.11; N, 8.79; S, 6.61.Brown powder, 60% yield; m.p. 189–191 °C; IR (KBr) νmax/cm−1 3107, 1638.46, 1601, 1543.18; 1H NMR (DMSO-d6) δ 7.74 , 7.44 , 7.36–7.32 , 7.30 , 7.19 , 7.05 , 5.67 , 4.04 , 3.84 , 3.82 , 3.36 ; 13C NMR (DMSO-d6) δ 43.70 (C4 pyrazoline), 56.03 (-OCH3), 56.11 (-OCH3), 63.94 (C5 pyrazoline), 104.41 (Ar-C), 109.59 (Ar-C), 112.09 (Ar-C), 115.72 (Ar-C), 115.94 (Ar-C), 120.69 (Ar-C), 123.94 (Ar-C), 127.85 (Ar-C), 127.93 (Ar-C), 129.02 (4 x C Ar), 131.54 (Ar-C), 131.57 (Ar-C), 132.46 (Ar-C), 141.40 (Ar-C), 149.31 (Ar-C), 149.84 (Ar-C), 151.19 (Ar-C), 153.55 (Ar-C), 160.83 (Ar-C), 163.26 (Ar-C), 164.99 (Ar-C). MS (APCI-) m/z (%) 494.2 [M+], 495.6 [M + 2]+; Anal. Calcd. For: C26H21N3O2SFCl (494): C, 63.22; H, 4.29; N, 8.51; S, 6.49; Found: C, 63.51; H, 4.45; N, 8.75; S, 6.42.Yellow powder, 68% yield; m.p. 203–205 °C; IR (KBr) νmax/cm−1 2966 , 1600 (C=N), 1545 (C=C); 1HNMR (DMSO-d6) δ 7.73 , 7.36–7.43 , 7.31 , 7.04 , 5.67 , 4.04 , 3.84 , 3.82 , 3.36 ; 13C NMR (DMSO-d6) δ 43.67 (C4 pyrazoline), 56.01 (4-OCH3), 56.09 (3-OCH3), 63.90 (C5 pyrazoline), 105.46 (Ar-C), 109.54 (Ar-C), 112.05 (Ar-C), 120.71 (Ar-C), 123.89 (Ar-C), 127.61 (2 x C Ar), 129.02 (6 x C Ar), 132.41 (Ar-C), 132.49 (Ar-C),133.76 (Ar-C), 141.30 (Ar-C), 149.29 (Ar-C), 149.64 (Ar-C), 151.19 (Ar-C), 153.63 (Ar-C),164.99 (Ar-C). MS (ESI-) m/z (%) 512.2 (M+ + 2); Anal. Calcd. For: C26H21N3O2S Cl2 (510.43): C, 61.18; H, 4.15; N, 8.23; S, 6.28; Found: C, 61.40; H, 4.29; N, 8.51; S, 6.37.White powder, 74% yield; m.p. 195–197 °C; IR (KBr) νmax/cmH-pyrazole-1-carbothioamide derivatives 3a, c (0.6 mmol) in absolute ethanol (10 mL), 3-chloropentane-2,4-dione (0.7 mmol) was added then refluxed at 80 °C for 4 h. Direct hot filtration proceeded and the obtained solid was washed with hot ethanol and recrystallized from EtOH/DMF to give the aimed compounds 9a–f.To a solution of 3--5-aryl-4,5-dihydro-1−1 3003 , 2928 , 1613 (C=O), 1542(C = C); 1H NMR (DMSO-d6) δ 7.32–7.24 , 7.05 , 5.75 , 4.04 , 3.838 , 3.82 , 3.36 , 2.39 , 2.37 ; 13C NMR (DMSO-d6) δ 19.11 (CH3), 30.02 (CH3), 44.15 (C4 pyrazoline), 56.04 (4-OCH3), 56.14 (3-OCH3), 63.32 (C5 pyrazoline), 109.61 (Ar-C), 112.04 (Ar-C), 121.23 (Ar-C), 123.41 (Ar-C), 123.97 (Ar-C), 126.20 (2 x C Ar), 128.06 (Ar-C), 129.28 (2 x C Ar), 141.79 (Ar-C), 149.31 (Ar-C), 151.6 (Ar-C), 156.23 (Ar-C), 157.78 (Ar-C), 165.11 (Ar-C), 189.48.(C= O); MS (ESI-) m/z (%) 420.2 [M-1]-; Anal. Calcd. For: C23H23N3O3S (421.52): C, 65.54; H, 5.5; N, 9.97; S, 7.61; Found: C, 65.78; H, 5.63; N, 10.15; S, 7.68.Greenish white powder, 63% yield; m.p 235–238 °C; IR (KBr) νmax/cm−1 1613 (C=O), 1535 (C=C), 1510; 1H NMR (DMSO-d6) δ 7.368 , 7.31 , 7.17 , 7.05 , 6.8 , 5.68 , 3.99 , 3.839 , 3.82 , 3.72 , 3.33 , 2.39 , 2.38 ; 13C NMR (DMSO-d6) δ 19.14 (CH3), 30.01 (CH3), 44.02 (C4 pyrazoline), 55.55 (OCH3), 56.03 (OCH3), 56.13 (OCH3), 62.90 (C5 pyrazoline), 109.56 (Ar-C), 112.02 (Ar-C), 114.57 (2 x C Ar), 121.20 (Ar-C), 123.50 (Ar-C), 123.86 (Ar-C), 127.62 (2 x C Ar), 133.72 (Ar-C), 149.32 (Ar-C), 151.58 (Ar-C), 156.20 (Ar-C), 157.78 (Ar-C), 159.12 (Ar-C), 165.08 (Ar-C), 189.37 (C=O); MS m/z (%) 451.39 [M+]; Anal. Calcd. For: C24H25N3O4S (451.54): C, 63.84; H, 5.58; N, 9.31; S, 7.1; Found: C, 64.09; H, 5.67; N, 9.48; S, 7.28.Greenish yellow powder, 75% yield; m.p. 213–215 °C; IR (KBr) νmax/cm−1 3003 , 2960 , 1617 (C=O), 1536, 1509; 1H NMR (DMSO-d6) δ 7.45–7.40 , 7.36 , 7.32 , 7.30–7.25 , 7.05 , 5.75 , 4.04 , 3.835 , 3.82 , 3.3 , 2.39 , 2.38 ; 13C NMR (DMSO-d6) δ 19.14 (CH3), 29.98 (CH3), 43.93 (C4 pyrazoline), 56.00 (OCH3), 56.11 (OCH3), 62.74 (C5 pyrazoline), 109.61 (Ar-C), 112.00 (Ar-C), 121.26 (Ar-C), 123.31 (Ar-C), 124.12 (Ar-C), 128.31 (2 x C Ar), 129.23 (2 x C Ar), 132.59 (Ar-C), 140.72 (Ar-C), 149.29 (Ar-C), 151.62 (Ar-C), 156.14 (Ar-C), 157.68 (Ar-C), 165.04 (Ar-C), 189.46 (C=O); MS (APCI+) m/z (%) 456.5 [M+], 458.7 [M + 2]+; Anal. Calcd. For: C23H22N3O3SCL (456): C, 60.59; H, 4.86; N, 9.22; S, 7.03; Found: C, 60.81; H, 5.08; N, 9.41; S, 7.14.Greenish yellow powder, 65% yield; m.p. 215–218 °C; IR (KBr) νmax/cm−1 1695 (C=O), 1637, 1507; 1H NMR (DMSO-d6) δ 7.38–7.24 , 7.04 , 5.72 , 4.19 , 4.05 , 3.84 , 3.82 , 3.3 , 2.35 , 1.25 ; 13C NMR (DMSO-d6) δ 14.79 (-CH2-CH3), 17.94 (CH3), 44.2 (C4 pyrazoline), 56.05 (OCH3), 56.13 (OCH3), 60.60 (CH2CH3), 63.40 (C5 pyrazoline), 109.61 (Ar-C), 110.61 (Ar-C), 112.03 (Ar-C), 121.20 (Ar-C), 123.43 (Ar-C), 126.21 (2 x C Ar), 128.04 (Ar-C), 129.27 (2 x C Ar), 141.81 (Ar-C), 149.31 (Ar-C), 151.56 (Ar-C), 155.94 (Ar-C), 159.73 (Ar-C), 162.34 (C=O), 165.21(Ar-C); MS (ESI−) m/z (%) 450.4 [M-H]−; Anal. Calcd. For: C24H25N3O4S (451.54): C, 63.84; H, 5.58; N, 9.3; S, 7.1; Found: C, 64.07; H, 5.69; N, 9.53; S, 7.23.Greenish yellow powder, 65% yield; m.p. 215–218 °C; IR (KBr) νmax/cm−1 1698 (C=O), 1601 (C=N), 1545; 1H NMR (DMSO-d6) δ 7.37 , 7.32 , 7.18 , 7.04 , 6.90  5.66 , 4.19 , 4.01 , 3.84 , 3.82 , 3.72 , 3.32 , 2.36 , 1.25 ; 13CNMR (DMSO-d6) δ 14.78 (CH2-CH3), 17.92 (CH3), 44.10 (C4 pyrazoline), 55.55 (OCH3), 56.05(OCH3), 56.12 (OCH3), 60.59 (CH2CH3), 63.01 (C5 pyrazoline), 109.58 (Ar-C), 110.39 (Ar-C), 112.02 (Ar-C), 114.57 (2 x C Ar), 121.20 (Ar-C), 123.50 (Ar-C), 127.64 (2 x C Ar), 133.67 (Ar-C), 149.31 (Ar-C), 151.55 (Ar-C), 156.02 (Ar-C), 159.11 (Ar-C), 159.61 (Ar-C), 162.33 (C=O), 165.11 (Ar-C); MS (ESI+) m/z (%) 482.6 [M + 1]+; Anal. Calcd. For: C25H27N3O5S (481.57): C, 62.35; H, 5.65; N, 8.73; S, 6.66; Found: C, 62.51; H, 5.83; N, 8.97; S, 6.81.Yellow powder, 77.5% yield; m.p. 169–171 °C; IR (KBr) νmax/cmνmax/cm−1 2992 , 1708 (C=O), 1602, 1571; 1H NMR (DMSO-d6) δ 7.42 , 7.35 , 7.27–7.32 , 7.04 , 5.72 , 4.19 , 4.04 , 3.832 , 3.817 , 3.34 , 2.35 , 1.25 ; 13C NMR (DMSO-d6) δ 14.78 (CH2CH3), 17.93 (CH3), 43.99 (C4 pyrazoline), 56.05 (OCH3), 56.13 (OCH3), 60.63 (CH2CH3), 62.82 (C5 pyrazoline), 109.62 (Ar-C), 110.69 (Ar-C), 112.03 (Ar-C), 121.24 (Ar-C), 123.34 (Ar-C), 128.31 (2 x C Ar), 129.24 (2 x C Ar), 132.50 (Ar-C), 140.74 (Ar-C), 149.30 (Ar-C), 151.60 (Ar-C), 155.95 (Ar-C), 159.67 (Ar-C), 162.31 (C= O), 165.15 (Ar-C). MS m/z (%) 486.26 [M+], 487.91 [M + 1]; Anal. Calcd. For: C24H24N3O4SCl (486): C, 59.32; H, 4.98; N, 8.65; S, 6.6; Found: C, 59.58; H, 5.06; N, 8.89; S, 6.72.Yellow powder, 61% yield; m.p. 197–200 °C; IR (KBr) 50 values were determined, and the viability at each concentration was determined in comparison to the control [Both breast cancer (MCF-7) and normal breast (MCF-10A) cells were purchased from National Research Institute, Egypt, and maintained in complete media of RPMI-1640 medium L-Glutamine . Cells were incubated following standard tissue culture work. Cell viability was measured 48 h later, utilizing MTT colorimetric assay  . Absorba control ,69.3c, 6b, 6e, 6k, and 9f against EGFR and HER2 kinases was determined using kinase inhibitory assays. Compounds’ autophosphorylation percentage inhibition was computed according to the following equation: 50 values were calculated using GraphPad prism software [The EGFR Kinase Assay Kit  and the HER2 Kinase Assay Kit  were used to evaluate the potential inhibitory activities against EGFR and HER2, respectively. The inhibitory potency of compounds software .5 cells/well) and incubated for 24 h. Subsequently, compounds 6k and 6e  were used to treat the seeded cells for 48 h. After that, the collection of the media supernatants and the cells was performed before undergoing rinsing with ice-cold PBS. The cells were suspended in 100 µL of annexin-binding buffer solution “25 mM CaCl2, 1.4 M NaCl, and 0.1 M Hepes/NaOH, pH 7.4” and incubated with “Annexin V-FITC solution (1:100) and propidium iodide (PI)” at a concentration equal 10 µg/mL in the dark for 30 min. Stained cells were then obtained by a Cytoflex FACS machine, and data analysis was performed using cytExpert software [MCF-7 breast cancer cells were seeded into 6-well culture plates  and antiapoptotic gene (Bcl-2) was evaluated using routine RT-PCR in the untreated and treated MCF-7 cells. Ct values were calculated to determine the relative genes expression in all samples by normalization to β-actin (housekeeping gene) ,74.The experimental protocol was approved by the Research Ethics Committee at Suez Canal University , Chemistry Department, Faculty of Science, Suez Canal University.50), experimental design, tumor mass, blood parameters, histopathological examinations were accomplished as previously described [6e and Lapatinib were completed, each at a dose of 10 mg/Kg BW/IP. Solid tumor masses’ weight and volume were measured at the end of the experiment. Animals from each group were sacrificed, and their blood was drawn to measure hemoglobin (Hb), red blood cell (RBC), white blood cell (WBC), and liver enzymes  levels.In vivo experiments for lethal dose . The results revealed that compounds 3c, 6b, 6e, 6f, 6k, and 9f displayed potent and selective anticancer activity against MCF-7 cell lines compared to lapatinib and were safe for the normal breast cell. Moreover, the highly potent compounds were investigated for their inhibitory properties against EGFR and HER2 compared to lapatinib. Interestingly, compound 6e showed potent EGFR and HER2 inhibitory effects , superior to the reference drug lapatinib . Moreover, compounds 6e and 6k also significantly induced apoptosis in the targeted breast cell line with 27.99% and 22.44%, respectively, compared to 0.68% for the control. Furthermore, the in vivo study for compound 6e showed a tumor inhibition ratio by 52.46% compared to 50.53% in lapatinib treatment. Molecular docking results revealed that compounds 6e and 6k had good binding affinity to the ATP binding site of EGFR with H bonds formed with the crucial amino acid residue Met793. Besides, compound 6e also anchored well in the HER2 binding site and participating in the crucial interaction with Thr862. According to the in vitro, in vivo, and in silico studies, compound 6e stands out as a potential drug candidate for the development of future anticancer agents, having a dual inhibitory activity against EGFR and HER2 receptors.Herein, a novel series of pyrazoline derivatives


There is compelling evidence that either centre-based or home-based pulmonary rehabilitation improves clinical outcomes in chronic obstructive pulmonary disease (COPD). There are known health service and personal barriers which prevent potentially eligible patients from accessing the benefits of pulmonary rehabilitation. The aim of this hybrid effectiveness-implementation trial is to examine the effects of offering patients a choice of pulmonary rehabilitation locations (home or centre) compared with offering only the traditional centre-based model.This is a two-arm cluster randomised, controlled, assessor-blinded trial of 14 centre-based pulmonary rehabilitation services allocated to intervention (offering choice of home-based or centre-based pulmonary rehabilitation) or control (continuing to offer centre-based pulmonary rehabilitation only), stratified by centre-based programme setting . 490 participants with COPD will be recruited. Centre-based pulmonary rehabilitation will be delivered according to best practice guidelines including supervised exercise training for 8 weeks. At intervention sites, the home-based pulmonary rehabilitation will be delivered according to an established 8-week model, comprising of one home visit, unsupervised exercise training and telephone calls that build motivation for exercise participation and facilitate self-management. The primary outcome is all-cause, unplanned hospitalisations in the 12 months following rehabilitation. Secondary outcomes include programme completion rates and measurements of 6-minute walk distance, chronic respiratory questionnaire, EQ-5D-5L, dyspnoea-12, physical activity and sedentary time at the end of rehabilitation and 12 months following rehabilitation.Direct healthcare costs, indirect costs and changes in EQ-5D-5L will be used to evaluate cost-effectiveness. A process evaluation will be undertaken to understand how the choice model is implemented and explore sustainability beyond the clinical trial.Alfred Hospital Ethics Committee has approved this protocol. The trial findings will be published in peer-reviewed journals, submitted for presentation at conferences and disseminated to patients across Australia with support from national lung charities and societies.NCT04217330. This hybrid effectiveness-implementation trial, conducted across 14 sites, will be the first to compare offering a choice of home-based or centre-based pulmonary rehabilitation to centre-based pulmonary rehabilitation only.The trial will only include participants with a diagnosis of chronic obstructive pulmonary disease (COPD).All participants, including those in the control group, will have the opportunity to achieve clinical benefits from an evidence-based pulmonary rehabilitation programme.It is possible that some individuals who are unable or unwilling to attend a centre-based programme may choose not to participate in the trial, as the choice of programme location is only offered after consent is given. Trial participants may therefore not be representative of all those who might benefit from pulmonary rehabilitation.The primary outcome is unplanned hospitalisation, the most valued outcome of patients with COPD.Chronic obstructive pulmonary disease (COPD) is characterised by persistent respiratory symptoms and airflow limitation.1Pulmonary rehabilitation is defined as ‘a comprehensive intervention based on a thorough patient assessment followed by patient-tailored therapies that include, but are not limited to, exercise training, education and behaviour change, designed to improve the physical and psychological condition of people with chronic respiratory disease and to promote the long-term adherence to health-enhancing behaviours’.11–13Despite compelling evidence for the benefits of pulmonary rehabilitation and unequivocal recommendations in COPD guidelines,21A low-cost model of delivering pulmonary rehabilitation directly into the home was developed (HomeBase) in Australia to improve access and uptake.23A recent ATS workshop report identified that the emergence of alternative models of pulmonary rehabilitation pose many unanswered questions for clinical practice, which will be best addressed by prospective clinical implementation trials.The aim of this hybrid effectiveness-implementation trial is to examine the effects of offering patients a choice of two pulmonary rehabilitation locations (home or centre) compared with offering only the traditional centre-based model. In order to achieve this aim, the objectives of the trial are threefold:To compare the clinical outcomes, acceptance and completion of pulmonary rehabilitation under the two service models ;To estimate the costs and compare the cost-effectiveness of the offer of choice of pulmonary rehabilitation location; andTo understand how the choice model is implemented and explore sustainability beyond the clinical trial.A two-arm cluster randomised, controlled, assessor-blinded trial will be conducted with 14 centre-based pulmonary rehabilitation services across Australia. The overall trial design is depicted in Included pulmonary rehabilitation services must provide outpatient programmes that admit at least 50 people with COPD each year and must be able to demonstrate established protocols for centre-based pulmonary rehabilitation according to national guidelines.Eligible pulmonary rehabilitation services will be randomised 1:1 to either the intervention group (offering choice of home-based or centre-based pulmonary rehabilitation) or control group (continuing to offer only centre-based pulmonary rehabilitation) stratified by setting of existing centre-based pulmonary rehabilitation . Such strata have been chosen to balance for potential factors related to implementation of choice of rehabilitation models  or differences in sociodemographic profiles in areas where hospital or non-hospital programmes are usually situated Research staff: Site principal investigators at each cluster will be notified of their allocation by the research team. Clinicians within sites who are involved in delivery of the intervention will not be blinded to group allocation. However, research staff within clusters that conduct post-intervention assessments and enter questionnaire data into the trial database will be blinded to group allocation.Participants: Participants will be blinded to group allocation. Patients will be informed that this study is examining the impact of pulmonary rehabilitation models on patient and health system outcomes. The specific nature of the models of interest, or the intervention group to which their site has been allocated will not be disclosed. As this is a study of behaviour (the impact of offering choice of pulmonary rehabilitation models), limited disclosure of the study aim regarding comparison of models is necessary. To truly understand and measure the impact of offering choice of programme models, the offer of choice in the intervention groups will be made after participant consent for the trial has been obtained, so that the choice can be recorded.To be eligible for inclusion at the 14 pulmonary rehabilitation services participants must (i) have a diagnosis of COPD confirmed on spirometry; (ii) be able to read, write and speak English; (iii) be able to provide informed consent. Exclusion criteria include: (i) attendance at pulmonary rehabilitation in the last 12 months; (ii) comorbidities which preclude exercise training Eligible participants will be identified through referrals to pulmonary rehabilitation programmes at participating clusters. To ensure that referrals are maximised across all sites, respiratory and general physicians at their affiliated hospitals will receive 3 monthly reminders to refer patients with COPD to pulmonary rehabilitation, consistent with current guidelines for best practice care (COPD-X).Participants in both groups will receive a copy of the ‘Better Living with Exercise’ booklet, produced by Lung Foundation Australia. The ‘Better Living with Exercise’ booklet assists people with COPD to work with their health professionals to optimise their exercise programme, based on their individual health conditions, goals and interests. All participants will also receive details to access an online version of the ‘Better Living with COPD; A Patient Guide’ on the Lung Foundation Australia website. The ‘Better Living with COPD: A Patient Guide’ (Third Edition)Pulmonary rehabilitation programmes assigned to the control group will offer eligible participants the opportunity to participate in their usual centre-based pulmonary rehabilitation, delivered according to current best practice guidelines.Pulmonary rehabilitation programmes assigned to the intervention group will offer eligible participants the choice of participating in an 8-week programme of either home-based or traditional centre-based pulmonary rehabilitation (see control group). Home-based rehabilitation will be delivered according to our protocol (HomeBase), which has been published in detail previously.Home visit: The programme commences with one home visit by a physiotherapist experienced in pulmonary rehabilitation during which exercise goals are established, the first exercise session is supervised and aspects of the self-management programme that cannot be adequately dealt with during subsequent telephone calls , are covered.Exercise training: Participants will follow an aerobic and strength training programme, which will be supervised for the first session and unsupervised thereafter. For the aerobic training, a walking programme will be prescribed with participants set a walking distance or number of steps to be completed in a given time via a pedometer and on the basis of a baseline 6-minute walk test . At least 30 min of aerobic exercise is recommended for each session, for at least five sessions per week. Strength training will use functional activities and equipment that are readily accessible in the home environment . Participants in the intervention group who choose home-based pulmonary rehabilitation will not have to return their pedometers at end of the programme and can use the device for the entire study duration.Home exercise diary: The initial goals set for aerobic and resistance exercise and details of the supervised exercise session during the home visit will be recorded in a home exercise diary. Participants will be encouraged to document all subsequent programme goals  and completed unsupervised exercise sessions, including duration and distance walked and the number and type of resistance exercises performed, in their diary each week. We have shown a significant relationship between diary-documented exercise sessions and device measured physical activity.Weekly telephone calls: Following the home visit in week one, each participant will receive seven once-weekly telephone calls. All calls will be delivered using a motivational interviewing approachEach participant will complete 8 weeks of rehabilitation and a follow-up of 12 months. Outcomes will be assessed at baseline, end of rehabilitation (after 8 weeks) and 12 months following the end of rehabilitation. We will undertake assessments with all participants  at 8 weeks and 12 months irrespective of whether a participant has completed their pulmonary rehabilitation programme. Participants will record any healthcare utilisation during the 12 months follow-up  in a diary. Each participant will be telephoned monthly during the 12 months by site research staff to encourage diary completion and capture patient-reported information on healthcare utilisation. These calls represent an additional contact with patients that would not be provided as part of routine clinical practice at trial sites, but the purpose of the call is only for research staff to support collection of data on healthcare use. Hospitalisation and use of other hospital services will be confirmed by medical record audit at 12 months, while 12-month medical and pharmaceutical resource use will be obtained from Services Australia using Pharmaceutical Benefits Scheme (PBS) and Medicare Benefits Schedule (MBS) data. Consent will be sought to use participants’ PBS and MBS data at the time of consent to the study. The schedule for assessments and follow-up is provided in All-cause, non-elective hospitalisation over 12 months: The number of participants admitted to hospital as an inpatient at least once in the 12 months following pulmonary rehabilitation will be compared between groups. Avoidance of hospitalisation is the research outcome that people with COPD value most highly and is of critical importance to the health system.Programme completion: The number of participants who complete their allocated pulmonary rehabilitation programme will be compared between groups at 8 weeks. Completion is defined as undertaking 70% of planned sessions in accordance with recent recommendations.6-minute walk distance (6MWD): The 6MWD is a validated measure of functional exercise capacity for COPD.Chronic Respiratory Questionnaire (CRQ): The CRQ is a disease-specific health-related quality of life measure with domains of dyspnoea, fatigue, mastery and emotional function. It is a patient-centred outcome that is responsive to change following pulmonary rehabilitation.EQ-5D-5L: This validated generic quality of life measureDyspnoea-12: This a global measure of breathlessness severity that captures both the physical and affective components of dyspnoea. It has excellent internal consistency, validity and reliability in COPD.44Physical activity participation: This is an important measure of behaviour change following pulmonary rehabilitation. We will measure time spent in moderate to vigorous physical activity and sedentary time, using the ActiGraph GT3X, a waist-worn, tri-axial accelerometer that has been validated in people with COPD.46Healthcare utilisation across 12 months: Healthcare utilisation will be recorded monthly in participant diaries and telephone record sheets kept by site research staff. We will confirm healthcare utilisation from hospital records, MBS and PBS data.The primary outcome is unplanned hospitalisation (inpatient admissions) in the 12 months following pulmonary rehabilitation. In our published Phase II trial,At 80% completion, our data indicate that 72% of the intervention group would remain free from admission at 12 months, compared with the observed rate of 57% in centre-based (control) participants.Generalised linear mixed models will be used to analyse the primary outcome of hospitalisation, and linear mixed models will be used for continuous outcomes. These will account for correlation between participants within a cluster. Analyses will be based on intention-to-treat and the level of significance will be set at p< 0.05.Economic evaluation will follow the protocol developed for our Phase II trial.22 23Cost comparisons: Cost comparisons will include comparison of per person costs, including direct  and indirect  healthcare costs for the two groups. Direct costs will include staff time, consumables, communications and overheads. Intervention costs will include staff inputs by duration, type and resource use (including troubleshooting and support) and equipment. Personal costs will include transportation, travel time and impact of the intervention on the economic activities of other household members. Health system costs will include visits to the general practitioner, specialist or emergency department; use of chronic disease services; and hospitalisation.Cost-effectiveness analysis: An incremental cost-effectiveness analysis will be undertaken to compare differences in costs between the two groups with differences in QALYs and number of hospitalisations in the 12-month follow-up period. For the former, a utility index will be calculated from the EQ-5D-5L by applying a ‘social tariff’, which then enables estimation of health benefits in terms of QALYs. The conversion to QALYs will be based on the assumption that for health states measured at successive points of time, the duration of each health state is exactly one half of the time interval between the two measurements.A process evaluation, informed by the Medical Research Council Guidance on Process Evaluations of complex interventions,This protocol  has received approval from Alfred Hospital Human Research Ethics Committee (379/19). Local research ethics committee approvals have also been obtained at sites where the National Mutual Acceptance scheme does not apply. Each site is also required to obtain local governance approval prior to commencement of data collection. Site investigators will be informed by the trial coordinator of any necessary modifications to the protocol and amendments to local ethics and/or governance approval. Potential participants at each site may be in a dependent relationship with pulmonary rehabilitation staff. For this reason pulmonary rehabilitation staff will only provide initial information about the trial to a potential participant, either at the time of initial assessment or over the telephone. If the potential participant is interested in receiving further detailed information, or providing informed consent, a researcher who is not in a dependent relationship with the potential participant will undertake this. The trial findings will be published in peer-reviewed journals and submitted to national and international conferences for presentation. The authorship of any publications will adhere to the guidelines established by the International Committee of Medical Journal Editors. We will disseminate results to patients with COPD across Australia through Lung Foundation Australia’s e-newsletter and website, and to health professionals through Thoracic Society of Australia and New Zealand’s e-news, scientific conferences and webinar programme.All sites will maintain an adverse event log for the trial period. An adverse event in the trial is being defined as any unfavourable, unintended diagnosis, symptom, sign , syndrome or disease that occurs during the trial, having been absent at baseline, or if present at baseline, appears to have worsened. A serious adverse event is defined as any adverse event leading to (i) death; (ii) serious deterioration in health resulting in a life-threatening illness or injury, or permanent impairment of a body structure or a body function; (iii) hospitalisation or prolongation of existing hospitalisation; (iv) medical or surgical intervention to prevent life-threatening illness; (v) fetal distress, fetal death or a congenital abnormality. Sites will inform the trial coordinator of any adverse event that necessitates a change to the protocol or participant information and consent form, is unexpected, or any serious adverse events. The trial coordinator will ensure the data and safety monitoring board (DSMB), relevant research ethics committees or governance offices are informed of any serious adverse events where necessary. The DSMB, consisting of a respiratory physician, two physiotherapists and a statistician, will make the final decision on the relatedness of any adverse events to the trial interventions or procedures. Unless otherwise determined by trial events, the DSMB will convene biannually.www.adeptrs.com), with encryption and password protection. No identifying information will be stored in the online database. Electronic data for all sites will be accessible by the coordinating principal investigator and the trial coordinator. Site specific investigators will only have access to data relating to their individual site.Hard copy original data collection forms will be stored in a locked filing cabinet within a locked office at each trial site. Electronic data will be stored centrally in a purpose-built on-line database  compared with offering only traditional centre-based models. Until recently, there has been very little ‘choice’ for patients referred to pulmonary rehabilitation; the delivery model of centre-based pulmonary rehabilitation has largely been a ‘one size ﬁts all’ that is inconsistent with the personalised approach of modern medicine.The adoption of alternative models for pulmonary rehabilitation will rely on demonstration of comparable or better clinical outcomes compared with those of traditional pulmonary rehabilitation programmes, which can be delivered in a cost-effective manner.Based on head-to-head trial evidence of home versus outpatient-based pulmonary rehabilitation, the 2017 Australian Pulmonary Rehabilitation guidelines recommended home programmes as an alternative to hospital-based programmes.This trial will provide the evidence needed to underpin future guidelines and policy decisions for pulmonary rehabilitation. If successful, the findings will inform a key change in the future of pulmonary rehabilitation services worldwide, where delivery includes more choices for patients and opportunities for greater personalisation of programmes.


Allergic rhinitis (AR) management requires a coordinated effort from healthcare providers and patients. Pharmacists are key members of these integrated care pathways resolving medication‐related problems, optimizing regimens, improving adherence and recommending therapies while establishing liaisons between patients and physicians.Allergic Rhinitis and its Impact on Asthma (ARIA) first published a reference document on the pharmacist's role in allergic rhinitis management in 2004. Several guidelines were developed over the past 20 years improving the care of allergic rhinitis patients through an evidence‐based, integrated care approach.This ARIA/EAACI/FIP Position Paper is based on the latest ARIA in the Pharmacy guidelines and provides: (a) a structured approach to pharmacists identifying people with AR and/or allergic conjunctivitis as well as those at risk of poor disease control; (b) an evidence‐based clinical decision support tool for optimising the management of allergic rhinitis in the community pharmacy; and (c) a framework of referral to the physician.This document is not intended to be a mandatory standard of care but is provided as a basis for pharmacists and their staff to develop relevant local standards of care for their patients, within their local practice environment. Pharmacy care varies between countries, and the guide should be adapted to the local situation. In some cases, these symptoms are spontaneously reversible, while, in others, they can be controlled by adequate treatment.Symptoms associated with AR have a significant impact on workAllergic conjunctivitis and asthma are common multimorbidities experienced by patients with AR.Most AR patients choose to manage their condition via self‐medication with non‐prescription medicines.Pharmacists may also assist in the identification of patients who are inappropriately self‐medicating resulting in a suboptimal treatment of their condition. They can also, if necessary, refer these patients for medical assessment.The Allergic Rhinitis and its Impact on Asthma (ARIA) guidelines, which were first released 20 years ago and are continually being updated with the latest evidence, provide a guide to the latest evidence‐based integrated care approach to the management of AR.2Pharmacists play an important role in confirming an AR diagnosis: some patients purchasing AR medicines in the pharmacy will have a diagnosis of AR by a physician, others will have an appropriate self‐diagnosis of AR, and the remainder no diagnosis or an incorrect oneAR symptoms may be similar to those of several conditions and confused with a viral infection such as the common cold/acute rhinosinusitis (including COVID‐19) or chronic rhinosinusitisnasal itching, rhinorrhoea, sneezing and eye symptoms is often consistent with allergic rhinitis. A mild‐to‐moderate loss of smell (hyposmia) may be present in the most severe patients, and can be sudden, severe and sometimes isolated in COVID‐19 patientsThe presence of 3Eye symptoms are common in AR patients. However, they are not experienced by all AR patients.The presence of conjunctivitis should always be assessed in patients with AR symptoms Table .Importantly, conjunctivitis is not always caused by an allergen Photophobia (light sensitivity), eye burning, dry eyes and unilateral symptoms are unlikely to be associated with Allergic Conjunctivitis and need a physician evaluation.4AR and asthma often coexist, and asthma should always be evaluated in a patient presenting with allergic rhinitis symptomsAR is a risk factor for the development of asthma.In patients with asthma, AR may be associated with poor control of the disease.5VAS “Nose” (0–10 cm): “How much are your nose symptoms bothering you today?”VAS “Eyes” (0–10 cm): “How much are your eye symptoms bothering you today?”The ARIA guidelines propose a classification of AR based on symptom control, quality of life, daily impact and duration. All of these can be combined into one question which relates to the degree to which the AR is bothersomeAllergic rhinitis may be intermittent or persistent, but this does not influence the treatment to be recommended. The ARIA guidelines base treatment recommendations on the impact of symptoms on day‐to‐day living6By comparing AR control at the first dispensing of an OTC medication with the evolution of control during treatment, the algorithm can help both the pharmacist and the physician to optimise treatment APPENDIX .https://www.mask‐air.com/).The monitoring and self‐management of AR can be supported through the MASK‐air AppThe cut‐off value for VAS “eye” is based on the results of the AR and the group's opinion, but has not been validated.7It is critical that people with AR should be followed‐up over time, to ensure that their treatment is appropriate and to identify patients who require a referral to their physician APPENDIX . Follow‐In considering how to assist the patient in long‐term management, it is important to realise that AR is undertreated and underdiagnosed and that patient self‐selection is profound.https://www.mask‐air.com/)).The pharmacist should continue to suggest to the patient that the monitoring and self‐management of AR can be supported through the MASK‐air AppOlga Lourenco: Conceptualization; Equal, Methodology; Equal, Writing – original draft; Equal, Writing – review & editing; Equal, Biljana Cvetkovski: Writing – original draft; Equal, Writing – review & editing; Equal, Vicky Kritikos: Writing – original draft; Equal, Writing – review & editing; Equal, Rachel House: Writing – original draft; Equal, Writing – review & editing; Equal, Sophie Scheire: Writing – original draft; Equal, Writing – review & editing; Equal, Elisio M Costa: Writing – original draft; Equal, Writing – review & editing; Equal, Joao Fonseca: Writing – original draft; Equal, Writing – review & editing; Equal, Enrica Menditto: Writing – review & editing; Equal, Anna Bedbrook: Writing – review & editing; Equal, Slawomir Bialek: Writing – review & editing; Equal, Vitalis Briedis: Writing – review & editing; Equal, Koen Boussery: Writing – review & editing; Equal, Giorgio Walter Canonica: Writing – review & editing; Equal, Tari Haahtela: Writing – review & editing; Equal, Piotr Kuna: Writing – review & editing; Equal, Joaquim Mullol: Writing – review & editing; Equal, Valentina Orlando: Writing – review & editing; Equal, Bolesław Samoliński: Writing – review & editing; Equal, Dana Wallace: Writing – review & editing; Equal, Catherine Duggan: Writing – review & editing; Equal, Ema Paulino: Writing – review & editing; Equal, Goncalo Sousa Pinto: Writing – review & editing; Equal, Lars‐Ake Soderlund: Writing – review & editing; Equal, Jean Bousquet: Conceptualization; Equal, Supervision; Equal, Writing – review & editing; Equal, Sinthia Bosnic‐Anticevich: Conceptualization; Equal, Methodology; Equal, Supervision; Equal, Writing – original draft; Equal, Writing – review & editing; Equal.Sinthia Bosnic‐Anticevich reports grants from TEVA, and personal fees from TEVA, AstraZeneca, Boehringer Ingelheim, GSK, Sanofi, and Mylan. João A. Fonseca reports personal fees from Viatris/Mylan. Piotr Kuna reports personal fees from Adamed, Berlin Chernie Menarini, Boehringer Ingelheim, Chiesi, AstraZeneca, Krka, Angellini, Novartis, Polpharma, Lekam, and GSK. Ema Paulino is the President of the Portuguese National Pharmacy Association and has been a Board member of the International Pharmaceutical Federation until October 2021. Biljana Svetkovski reports personal fees from GSK and Sanofi. Dana Wallace reports personal fees from Optinose, ALK, and Sanofi, and was a primary author on the JTFPP Rhinitis Practice Parameter Update 2020.Supplementary MaterialClick here for additional data file.


P), number of hydrogen-bondacceptors (nHA), number of rotatable bonds (nRot), and topologicalpolar surface area (TPSA) than molecules from intermediate/inactiveclass. The chemical space visualization in the principal componentanalysis (PCA) plot shows significant overlapping distributions betweenpotent/active class molecules and intermediate/inactive class molecules;potent/active class molecules are intensively distributed, while intermediate/inactiveclass molecules are widely and sparsely distributed. Murcko scaffoldanalysis has shown low scaffold diversity in general, and scaffolddiversity of potent/active class molecules is even lower than intermediate/inactiveclass molecules, indicating the necessity for developing moleculeswith novel scaffolds. Furthermore, scaffold visualization has identified16 representative Murcko scaffolds. Among them, scaffolds 1, 2, 3,4, 7, 8, 10, 11, 15, and 16 are highly favorable scaffolds due totheir high scaffold enrichment factor values. Based on scaffold analysis,their local structure–activity relationships (SARs) were investigatedand summarized. In addition, the global SAR landscape was exploredby quantitative structure–activity relationship (QSAR) modelingsand structure–activity landscape visualization. A QSAR classificationmodel incorporating all of the 1678 molecules stands out as the bestmodel from a total of 12 candidate models for AR antagonists .Deeper insights into the structure–activity landscape highlighteda total of seven significant activity cliff (AC) generators , which provide valuable SAR information for medicinal chemistry.The findings in this study provide new insights and guidelines forhit identification and lead optimization for the development of novelAR antagonists.Prostate cancer (PCa) is a major leading cause of mortalityofcancer among males. There have been numerous studies to develop antagonistsagainst androgen receptor (AR), a crucial therapeutic target for PCa.This study is a systematic cheminformatic analysis and machine learningmodeling to study the chemical space, scaffolds, structure–activityrelationship, and landscape of human AR antagonists. There are 1678molecules as final data sets. Chemical space visualization by physicochemicalproperty visualization has demonstrated that molecules from the potent/activeclass generally have a mildly smaller molecular weight (MW), octanol–waterpartition coefficient (log  Localized diseases can be asymptomaticor mild symptoms, which can be tackled by active surveillance only.At this stage, the 5 year survival rate is nearly 100%. Then, a smallpercentage of cases proceed to locally advanced diseases, which aredefined as the cancer tissues expanding beyond the prostate capsulebut showing no lymph node spread or metastasis. For localized advancedcases, androgen deprivation therapy (ADT) through medication or surgerycan slow down the progress of the disease. However, after the medianof 24 months of ADT, treatment resistance is inevitable as demonstratedby a relapse of the biomarker: serum prostate specific antigen (PSA)levels. In the case of this, PCa progresses to castration-resistantprostate cancer (CRPC). And the 5 year survival rate is only 30%.There are a number of mechanisms underlying the pathogenesis and progressionof PCa, and most are associated with androgen synthesis and androgenreceptor (AR) signaling pathways.Prostate cancer (PCa)is the second most prevalent among male cancerpatients, as well as the fifth leading cause of mortality of canceramong males. There were 1,276,106 new cases causing 358,989 deathsin 20183 The AR is bound to heat shock proteins  and molecularchaperones at the cytosol at its dormant state.5 Whenbound by an androgen molecule, it is released from the HSPs, transferredto the nucleus, and undergoes homologous dimerization to enter itsactive state as a transcriptional factor. AR is the pivotal regulatorfor PCa pathogenesis and progression. In most PCa cases, AR is overexpressedand is the driving force for the disease progression to CRPC. Therefore,it has become a highly significant therapeutic target for the drugdiscovery against PCa.7 AR has 919 amino acid residuesand consists of three major domains: N-terminal domain (NTD) (residues1–555), DNA binding domain (DBD) (residues 555–623),and the C-terminal ligand binding domain (LBD) (residues 665–919),which is connected to the DBD by a flexible hinge region (residues623–665). All three domains are important for receptor function.The highly conserved DBD tethers the AR to promoter and enhancer regionsof AR-regulated genes by direct DNA binding to allow the activationfunctions of the NTD and LBD, so that these genes can undergo transcription.Currently, there is no crystal structure for the full-length AR. However,the structures of both the DBD and LBD have been identified separately.910 Various small molecules that act as AR agonists or AR antagonistsexert their roles by binding to the ligand binding pocket in the LBD.LBD is currently the crucial target for the drug discovery of AR antagonists.Androgen receptor (AR) isa steroid receptor of the nuclear receptorsuperfamily. It functions as a transcription factor and regulatesthe development and growth of the prostate.11 as shown in 3 However,they have relatively weak binding affinities with AR, without capabilitiesof complete blockade of AR. In addition, they can induce mutationsof LBD of AR along with treatment duration, leading to partial agonismto the mutated AR. Therefore, they are gradually replaced by the second-generationantagonists represented by enzalutamide, apalutamide, and darolutamide.In addition to the competitive inhibition of androgen-AR binding,second-generation antagonists also inhibit the AR translocation fromcytoplasm to cell nucleus, the coactivator recruitment, and the AR-DNAbinding.12 Although the second-generationantagonists display advantages over the first-generation antagonists,they have inevitably induced AR-resistant mutations that can renderthese drugs partial or mixed agonists for AR.138 first,the discovery of more potent molecules against LBD of AR by chemicalmodification of marketed drugs; second, due to the limited scaffolddiversity of currently available antagonists against LBD of AR, moredesirable chemical scaffolds need to be discovered and tried; lastbut not least, to try alternative domains of AR, such as NTD, as thebinding site of novel antagonists.To treat PCa, especially CRPC, there have beennumerous studiesto develop AR antagonists. By chemistry, AR antagonists can be dividedinto two categories: steroidal antagonists and nonsteroidal antagonists.Cyproterone acetate is the representative steroidal AR antagonist,14 In drug discovery, cheminformatics has longbeen applied to aid in the search for and optimization of new molecules.This paper performs systematic cheminformatic analysis and modelingto facilitate drug discovery of future AR antagonists. All of thedata sets are compiled from the ChEMBL database. Cheminformatic analysesare performed to visualize the chemical space, investigate the distributionand patterns, identify representative Murcko scaffolds, and clarifystructure–activity landscape. In addition, machine learningtechniques are used to build quantitative structure–activityrelationship (QSAR) classification models to better predict AR antagonisticbioactivities.Cheminformatics is a multidisciplinary field byutilization ofcomputational and information technologies to find solutions for awide range of problems in chemistry. It has achieved exponential progressin the era of machine learning and artificial intelligence (AI).250 areselected, and as a result, there are 3266 molecules obtained. Thencomes the data cleansing process. First, 783 data sets that are withoutessential values (data sets that are without IC50 and pIC50 and those without “=”) are removed, so that2483 data sets are left. Then, redundant data sets are removed bythe ChEMBL molecule ID. In this step, a total of 805 duplicate datasets are removed. As a result, there are 1678 molecules left for thedata set. Among them, there are 161 steroidal molecules and 1517 nonsteroidalmolecules. Next, molecules with pIC50 ≥ 8 are labeledas potent, pIC50 between 7 and 8 as active, while 6 ≤pIC50 < 7 as intermediate, and pIC50 <6 as inactive. As a result, there are 122 potent, 432 active, 604intermediate, and 520 inactive molecules.As a computationalstudy, all of the biological activity data ofthe AR antagonists were compiled from ChEMBL database . Bioactivities labeled with ICFor the cheminformaticanalysis, all of the data sets are keptat original sizes. In the QSAR classification modeling stage, datasets are further balanced via the random oversampling technique. Namely,data sets randomly selected are duplicated within the potent, active,and inactive classes until their sizes are equal to the size of intermediatedata (604 entries). The random state for oversampling is set to 42to maintain reproducibility.2.12.1.1P), number of hydrogen-bond acceptors(nHA), number of hydrogen-bond donors (nHD), number of rotatable bonds(nRot), and topological polar surface area (TPSA). DataWarrior15 (version 5.5.0) is used for the calculationof these properties.In this step, all of the moleculesare defined as group 1 (potent and active classes) and group 2 (intermediateand inactive classes). A total of six physicochemical properties arecalculated, visualized, and compared between group 1 and group 2:molecular weight (MW), octanol–water partition coefficient,jupyter notebook.In this section, the maximal, minimal,median, mean, skewness, and kurtosis are analyzed for the descriptors,and 2.1.215 (version 5.5.0) is used for PCA.In this study, the six physicochemical properties are dimensionallyreduced by principal component analysis (PCA). DataWarrior2.22.2.150 levels, so that the distributionpatterns of scaffolds can be identified and further analyzed. In addition,the frequency of skeletons and scaffolds is calculated and ranked.DataWarrior (version 5.5.0) is used for Murcko scaffold generationand visualization.Inthis study, Murcko scaffolds and cyclic skeleton systems are obtainedand compared by pIC2.2.2Murcko scaffold diversity is calculated as the proportion of thenumber of Murcko scaffolds, number of singleton scaffolds (scaffoldthat possesses a single molecule), and number of Murcko skeletonsto the total number of molecules.2.2.316 Themolecular scaffolds with the higher EF are more desirable and viceversa.Scaffold enrichment factor (EF) is the ratio of the proportionof active molecules with a given scaffold to the proportion of activemolecules in the entire data set.2.317 The thresholdof structure and activity similarity are set to 0.9 and 2, respectively,which indicates that the activity cliff quadrant is defined as X > 0.9 and Y > 2. And the molecularfingerprintsused for generating the SAS map consist of ECFP4, PubChem, and MACCS.Each set of fingerprints can generate the corresponding SAS maps andACs, and the overlapping ACs are defined as consensus ACs.In this study, the SAS map and SALI value are used to visualizethe structure–activity landscape and identify activity cliffs(AC)s. In this study, Activity Landscape Plotter V.1, a webserverto generate SAS maps, is used.2.4The schematic diagram for the modeling process is shown in 2.4.118 The fingerprint set contains881 binary representations of the chemical structural fragments usedby PubChem. The parameter for the PaDEL package is set to detect aromaticity:true; standardize nitrogen: true; standardize tautomers: true; threads= 2; remove salt: true; log = true; fingerprints = true.PubChemfingerprints provided by the PaDEL package (PaDELpy-0.1.13) were usedfor modeling.2.4.2Features withvariance lower than 0.1 and features demonstrating high correlation(>0.95) were removed. As a result, after feature selection of the881 features, there are 223 left after the removal of low-variancefeatures and 145 features were left after removing high correlationfeatures.2.4.321 have been used independently for model construction as shown in For all12 models, the ratio of the training set and testing set is set to80:20. Within the training set, a 10-fold cross-validation was performedto guarantee the robustness and reliability of the model. In addition,a one-vs-rest (OVR) strategy is employed for the multiclass classification.To get the best model, 12 representative classification algorithms2.4.4The performance of the QSAR classification models was evaluatedvia three parameters: the accuracy (ac), the recall (re), and Matthew’scorrelation coefficient (MCC). As a multiclass classification model,the recall is calculated by macroaverage. Let TP, TN, FP, and FN denotetrue positive, true negative, false positive, and false negative,respectively. The accuracy 1, recall2.550 values tostudy their roles in determining bioactivities.The Murcko scaffolds extracted previouslyare used for the automatic SAR in the DataWarrior platform (version5.5.0). In this analysis, based on each specific scaffold, the R groupson different molecules are compared by pIC3Additional File.As shown in 3.1P, nHA, nRot, and TPSA. The skew ofgroup 1 has a much higher value in MW, nHA, and TPSA, which meansthat the distribution of these properties is more skewed. The kurtosisof group 1 has much higher values in MW, nHA, nHD, nRot, and TPSA,meaning that the distributions are highly peaked, heavily tailed,and leptokurtic, and the properties in group 1 are intensely distributedwithin a small region. It is important to note that exploratory dataanalysis by property distribution patterns is the preliminary visualizationof chemical space. There are six properties or six dimensions to analyzethe molecules. To get further insight into the chemical space, dimensionalityreduction techniques are required to get more straightforward visualizations.Principal component analysis, or PCA, is an unsupervised machine learningapproach used to reduce the dimensionality of large data sets by transforminglarge data sets into smaller data sets that still contain most ofthe information of the large set. In PCA, sets of correlated variablesin a higher-dimensional space are combined to produce a set of variablesin a lower-dimensional space.19Exploratorydata analysis aims to explore the AR antagonists’ physicochemicalproperty ranges, distributions, and patterns to get an overview ofchemical space. It is the beginning of this study. As shown in 3.2P (−0.814) is the most significantnegative contributor. The third PC has the highest loading by nHA(0.186) and MW (0.147), and nHD (−0.965) is the most significantnegative contributor.The PCA plot with six physicochemical properties has shown that mostlythe potent and active class molecules are contained in chemical spacefrom intermediate and inactive classes. Potent or active class moleculesthat are not contained show overlapping with intermediate, inactiveclasses 6.The poIn 3.3Scaffold analysisconsists of three aspects: scaffold visualization, scaffold diversityanalysis, and scaffold correlation with bioactivities. According tothe visualization and frequency ranking, the top five most frequentCSKs are listed in Scaffold diversity is calculated as the proportionof the numberof scaffolds to the total number of molecules 4. The reMurcko scaffold diversity analysis has demonstratedthat scaffolddiversity is low in general, and scaffold diversity of potent, activeclass molecules is lower than intermediate, inactive class molecules.Therefore, there is an urgent need to find more novel scaffolds forAR antagonists.To correlate scaffolds with bioactivities, Murckoscaffolds arealso plotted against bioactivity values to identify favorable scaffolds.Based on the plot, scaffold frequencies and EFs are calculated. Thereis a total of 16 representative scaffolds with either high frequenciesor high EF values. Scaffolds 1–4 all belong to tricyclic 1skeleton and they together have the highest frequency of moleculesand high EF values, so that they are the most favorable scaffolds.In addition, scaffolds 10, 11, 14, and 15 all have a high frequencyof molecules as well as high EF values. Scaffold 5 belongs to tricyclic3 skeleton. In this study, scaffold 5 has EF < 1, which means thatmolecules with scaffold 5 have a lower proportion of potent/activeclasses than the overall average proportion. On the other hand, somerepresentative second-generation AR antagonist drugs such as enzalutamideand apalutamide belong to scaffold 5. Therefore, the low EF valuein scaffold 5 just represents the molecules compiled by this study.The role of scaffold 5 in the second-generation AR antagonist drugshas determined its significance in further lead optimization.From this section, the structural in-depth information on AR antagonistshas been extracted. To explore the activity information and to correlatewith the structural information, SAR is explored using the R-groupdecomposition. From this procedure, local SAR information and valuablemedicinal chemistry information are revealed for further chemicalmodifications. The SAR information is summarized in In 3.420 Beforeexecuting QSAR modeling, a prerequisite is to evaluate the modelabilityof the data sets. In this study, structure–activity landscapevisualization by the SAS map to evaluate modelability is performed,rough region of activity cliffs (upper right), nondescript region (lowerleft), and scaffold hopping region  (upper right). SALI value is short for thestructure–activity landscape index. It is a pairwise measurebetween activity difference and structural difference for each pairof compounds. The higher SALI value, the higher potential of the pairof compounds forming ACs. In the SAS map, red color is used to highlighthigh SALI value pairs against green color, which indicates low SALIvalue. The SALI value is given in eq A meansactivity, sim for similarity, and m1 and m2 are the abbreviations for molecule 1 and molecule 2,respectively. In this study, activity is represented by the pIC50 values of molecules, and similarity is represented by PubChemfingerprint similarity. In addition, the SALI value is utilized toquantitatively determine the existence of activity cliffs. SAS maphas revealed that only a small percentage of pairs of compounds showa discontinuous structure–activity relationship. Based on theSAS map visualization and SALI values, it is concluded that the ARantagonist data sets are feasible to build the QSAR model.QSAR is short for the quantitative structure–activityrelationship. It is a mathematical model to correlate structural datasets (PubChem fingerprint information in this study) of moleculeswith biological activities .erformed8A. SAS mSupporting Information. Among them, four molecules belongto tricyclic 1 Murcko skeleton. And each of the first- and second-rankedAC generators forms 28 and 13 ACs. There are lucrative molecules forfurther drug design.In addition to QSAR modeling, according to the SASmap data set,there are 136 pairs of molecules that satisfy the threshold of ACs.Among the 136 pairs of molecules, there are seven significant AC generators that can be seen as molecules with which they form a numberof pairs of ACs. Activity cliff generators were identified as moleculeshighly frequent among ACs. The presence of ACs and AC generators iscounterproductive to build machine learning models; however, theyare of particular interest in medicinal chemistry to optimize leadcompounds. The seven AC generators are listed in the The above are the results of this studyand brief interpretations.A comprehensive understanding and interpretation of the outcomes ofthis study require discussing the current progress and knowledge gapsof AR antagonist drug discovery and comparing them with previous studiesin computational drug discovery. In addition, the limitations of thestudy should be discussed, as well.3.522 ovarian cancer,23 pancreatic cancer,24 and even bladdercancer.25 Last but not least, AR signalingis a crucial pathway for human development and skeletomuscular integrity.26 Based on the role in various diseases or clinicalimplications, ligands of AR are generally divided into AR agonists,selective androgen receptor modulators (SARM),27 and AR antagonists. AR signaling is the driving forcefor the growth and progression of PC. At its dormant state, AR formsa complex with heat shock proteins  and molecular chaperonesat the cytosol. When bound by androgen or other agonists, it undergoesa conformational change that leads to its release from the HSPs. Withthe assistance of coactivators, AR is then transferred to the nucleus,recognizing androgen response elements (AREs) in a homologous dimerizedform, entering its active state to regulate the expression of downstreamgenes. In addition to PC, AR signaling is associated with a seriesof hormone-related malignancies, such as breast cancer,12 However,there are two challenges for second-generation antagonists: first,enzalutamide and apalutamide have off-target effects on the GABA-areceptors in the central nervous system and tend to induce seizuresin a portion of patients. Second, like the first generation, theyinevitably induce drug resistance in the long term. The mechanismsof resistance include point mutations , AR alternative splicingleading to the absence of LBD on AR isoforms, and AR genetic amplificationand enhanced transcription.8 Although thereare some adaptations for second-generation antagonists as treatmentregimens, the mechanisms that lead to resistance to first-generationantagonists inevitably render them useless. It is important to mentionthat second-generation AR antagonists share the same scaffold andthe lack of scaffold diversity in antagonists could accelerate theproceedings of resistance. Based on the current challenges and situations,there are three recommendations for developing novel AR antagonists:(1)Chemical optimization of the currentlyavailable molecules to obtain novel AR antagonists that can overcomeor avoid resistance, largely targeting LBD;(2)Using scaffold hopping and virtualscreening to find novel AR antagonists of more diverse scaffolds;(3)Focusing on alternativebinding sites,such as NTD and DBD of AR, to overcome the drug resistance issue.There is a great deal of work associated with drugdiscovery forAR antagonists. Currently, until the year 2020, there have been twogenerations of AR antagonists approved for clinical implications.First-generation antagonists were dated back to the 1990s, i.e., flutamide,nilutamide, and bicalutamide. With the use of first-generation antagonists,patients eventually proceed to CRPC. Second-generation antagonists havebeen approved since 2012, and they have much more potent binding affinitieswith AR than the first-generation antagonists.28 Phase II clinical trial  for mCRPC patients in the United States is expected tobe complete by the end of 2022. It is revealed that GT-0918 as anorally available novel candidate drug acts not only by antagonizingAR but also by downregulating the lipogenesis through inhibiting theexpression of ATP citrate lyase (ACL), acetyl CoA carboxylase (ACC),fatty acid synthase (FASN), and sterol regulatory element-bindingprotein-1 (SREBP-1).29 The coinhibition of AR signalingpathway and lipogenesis process confers GT-0918 more promising prospectusto overcome drug resistance and benefit patients’ survival.TRC-253, known as JNJ-63576253, is a novel, orally available novelcandidate drug that has completed phase II/A clinical trial in Nov,2020.30 As a lucrative pan-inhibitor againsta series of mutant ARs as well as wild-type ARs, it is promising tobe a novel resistance-overcome AR antagonist in the future. Rezvilutamide,known as SHR3680, is a novel candidate drug against AR that has completedphase II/A clinical trials and is proven to be a potent AR antagonist with reduced CNS distribution,thereby decreasing the risk of inducing seizures in patients. Rezvilutamideis proven to be another promising candidate drug that is worth furtherclinical trials. TQB3720, a novel AR antagonist, is undergoing phaseI clinical trial , and itis expected to be completed in 2023. BMS-641988 was used to be a promisingcandidate drug;31 however, phase I clinicaltrial  has provenits potential to induce seizure in patients and further clinical trialshave been discontinued. Among the emerging candidate drugs, proxalutamide,TRC-253, and rezvilutamide share the same scaffold structure withapproved second-generation drugs, while BMS-641988 has a distinctscaffold structure. There should be more diverse scaffolds to facilitatemore drug discoveries.At the moment of this study, there are some novel ARantagonistcandidates that have already proceeded to phase I/II clinical trials.Proxalutamide, also known as GT-0918, which shares the same scaffoldwith second-generation antagonists, has passed phase I clinical trial with a 3-fold more potentbinding affinity than enzalutamide and satisfactory tolerance.3 Untilnow, there are several novel candidate drugs that antagonize AR functionsvia the NTD. They are the EPI-001, EPI-002 (EPI-506 as its prodrug),and VPC-220010.34 EPI series are natural products extracted from marinesponges. They are proved to be AR antagonists through NTD, as brandnew target areas compared to other AR antagonists. Phase 1 clinicaltrial with EPI-506 has been terminated due to poor bioavailabilityand potency; however, safety profiles were satisfactory.32 Although the EPI-506 trial was terminated, itis a promising core structure for chemical modifications to find optimalNTD targeting AR antagonists. NTD as an intrinsically disordered domainon AR is not suitable for structure-based drug discovery for now;however, ligand-based optimization is highly recommended for furtherinvestigations. DBD is another domain on the AR that could be an alternativebinding domain. DBD consists of two α-helices: P-box for recognitionthat binds transcription factor and D-box for AR dimerization. However,due to the fact that both the P-box and D-box motifs are highly conservedbetween steroid receptors, drug discovery for AR targeting of thisdomain can easily trigger off-target effects on other steroid receptors,such as progesterone receptor (PR) and glucocorticoid receptor (GR).Although challenging, there is some progress in DBD targeting AR antagonists,such as the discovery of pyrvinium, an anti-pinworm infection drugrepurposed for AR antagonism, as a noncompetitive antagonist via DBD.As expected, pyrvinium demonstrated various antagonisms for PR andGR. Apart from this molecule, there are some other studies on thebinding site, but none of them have proceeded to clinical trials fornow.37Currently, all of the clinically approvedAR antagonists exertfunctions through the LBD of AR and the vast majority of candidatedrugs are targeting LBD, as well. They are vulnerable to LBD pointmutations and alternative splices. Apart from LBD, another domainon the AR, the NTD, is a lucrative novel target on AR, thanks to itsessential role in AR transcription.3.638 And their study covered both AR agonists andantagonists. The group of Ban et al. has worked on computational drugdiscovery for AR antagonists using a structure-based drug discoveryapproach.39 The group of Paul et al. hasdeveloped a model that can predict the response of previously unreportedAR mutants to current treatment pipelines for PCa,40 with an accuracy of 90%. Their model enables the predictionof the response of AR mutants by various ligands to see whether theyact as agonists or antagonists. Their model has already been validatedby external experimental evaluation. Apart from that, there are anumber of studies focusing on QSAR/machine learning modelings of ARagonists/antagonists.44 Among the previous machine learning modeling studies, the projectof CoMPARA, namely, the collaborative modeling project for androgenreceptor activity, initiated by the U.S. Environmental ProtectionAgency (EPA) to explore endocrine-disrupting chemicals (EDC)s, isthe most prominent project with large amounts of high-quality datasets of various origins, rigorous consensus model based on a totalof 91 externally submitted models, and reliable model performanceof 80% accuracy.42 However, the CoMPARAproject is focused on evaluating environmental chemicals and toxicantsthat may play the roles of EDCs and is not applicable to drug discovery.In comparison with previous similar studies, this study uses datasets from the ChEMBL database, using a ligand-based drug discoveryapproach and cheminformatics, mainly by scaffold analysis, SAR, andQSAR modeling.There have been only a few previousstudies using cheminformatic or computational methods to identifynovel AR antagonists. The group of Hao et al. performed systemic cheminformaticanalysis for AR agonists and antagonists using data sets from PubChem.3.7As a computationalstudy, all of the data sets of this study are compiled from the ChEMBLdatabase, and they are retrospective records originating from variousyears and backgrounds for the bioactivities. Some previous and ongoingassays and experiments are not recorded in the ChEMBL database, sothat there could be a lack of some valuable data sets, for example,additional valuable scaffolds, which is the main limitation of thestudy.4Androgen receptor signalingis the driving force for the growthand progression of prostate cancer. Until now, there has been a greatdeal of work associated with drug discovery for novel AR antagonists.This study is a systematic cheminformatic analysis and machine learningmodeling to visualize the chemical space, analyze the Murcko scaffold,and investigate the structure–activity relationship and landscapeof human androgen receptor antagonists from the ChEMBL database. Thegraphical summary of this study is shown in