Oroxylum indicum (O. indicum) is an important traditional Chinese medicine that exerts a wide spectrum of pharmacological activities. However, the pharmacological effect of O. indicum and its mechanism of action have not to be systematically elucidated yet. In this study, the druggability for active compounds of O. indicum was assessed via Traditional Chinese Medicine Systems Pharmacology Database (TCMSP), and the potential drug targets of O. indicum were identified using PharmMapper database. Additionally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed via WebGestalt. Drug-target-pathway networks were constructed using Cytoscape to give a visual view. Our findings revealed that O. indicum has extremely superb druggability with 41 putative identified target genes. GO, KEGG, and network analyses showed that these targets were associated with inflammatory immunoreactions, cancer, and other biological processes. In summary, O. indicum is predicted to target multiple genes/proteins and pathways that shape a network which can exert systematic pharmacological effects. Oroxylum indicum (O. indicum), for instance, is an important herbal medicine that belongs to the Bignoniaceae family [Oroxylum indicum belongs to the Bignoniaceae family [Traditional Chinese medicine (TCM) is the most plentiful source of bioactive compounds or pharmaceutical components for drug development . Oroxylue family  and has e family . It is ke family  in Chinae family , 6. Oroxe family  and posse family , antimice family , antialle family , anti-ine family , and ante family –12 propeOroxylum indicum owing to its underlying roles in the prevention and clinical therapy of a wide spectrum of diseases, for instance, cough, asthma, pertussis, pharyngitis, chronic or acute bronchitis, arthritic and rheumatic problems, and tumors [Oroxylum indicum could be used as a valuable TCM by reason of the incision of complicated pathophysiological processes, identification of therapeutic targets, and discovery of potential molecular functions and involved pathways. However, the possible molecular mechanisms that Oroxylum indicum induces are rarely investigated. Simultaneously, utilization of computational approaches to identify the drug target molecules and uncover the hidden mechanisms is becoming the main current for the sake of saving time, money, and effort. Especially important, computational target identification and the following molecular mechanisms could speed up the progress of drug development.Attentions have been closely paid to d tumors , 3, 12. Oroxylum indicum with the employment of computational approaches. Firstly, the active compounds of Oroxylum indicum were screened and evaluated using the Traditional Chinese Medicine Systems Pharmacology (TCMSP) resource. Next, the potential target genes of those active compounds were predicted by the PharmMapper database. Moreover, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted with the utilization of the predicted targets. Finally, the drug-targets and targets-pathways pharmacological data of Oroxylum indicum were integrated and further used to construct a three-layer network. An overview of the analytical procedures for Oroxylum indicum target gene identification and mechanism investigation is illustrated in Therefore, we systematically investigated the pharmacological functions of http://lsp.nwu.edu.cn/tcmsp.php) is a systematic database of pharmacology for natural compounds or TCM [TCMSP  is an online reverse-docking database for potential target identification of small molecules [PharmMapper (olecules . Given ahttp://www.genemania.org) is an online tool for generating hypotheses concerning gene function, analyzing gene sets, and prioritizing genes for functional assays [GeneMANIA  to systematically investigate the functions and pathway enrichment information on the target genes we predicted [p value less than 0.05 was considered to be significant statistically.We employed a Web-based gene set analysis toolkit  to build and analyze the drug-targets-pathways networks.Oroxylum indicum were identified based on the ADME-related properties from the TCMSP database, with the threshold OB ≥ 40% and DL ≥ 0.2. 10 active compounds were yielded and finally chosen for further investigation  and druglikeness (DL). The active compounds of tigation .Oroxylum indicum were identified using PharmMapper [Potential targets for the 10 active compounds of rmMapper  with selAmong the 41 target genes and their interacting genes, it was uncovered that 40.32% displayed coexpression characteristics, 31.43% had physical interactions, and 12.96% engaged in colocalization. Other results, including shared protein domains, pathway, and genetic interactions, are illustrated in p value including pathways in cancer, complement and coagulation cascades, and apoptosis . As demonstrated in In silico analyses can improve pharmacokinetic modeling, prediction, as much as toxicity, and metabolic endpoints; all of which streamline and speed up the drug development progress [Traditional Chinese medicine (TCM) has been applied extensively to prevent and treat various kinds of diseases owing to its high efficiency, no drug resistance, and low toxicity , 23. Thuprogress , 29.in silico target identification approaches have been developed and are widely used towards this end. As shown in Oroxylum indicum were identified using computational approaches. GeneMANIA analysis, with information on coexpression, physical interactions, and colocalization, as well as pathway, shared protein domains, and genetic interactions, indicated that these target genes and their interacting genes may have identical or similar functions.Identification of target genes is the first step in drug discovery. It has been revealed that more and more active drugs or compounds interact with multiple genes or proteins to exert their pharmacological functions –33. DiffOroxylum indicum in pharyngitis and chronic or acute bronchitis. For example, the majority of the identified targets of Oroxylum indicum were enriched in biological regulation, response to stimulus, cell communication, and so on. These were all essential for Oroxylum indicum to exert its inflammatory role. Similarly, Begum et al. also found Oroxylum indicum plays a vital role in inhibiting inflammation, antiulcerative, enhancing immunity, and lowering blood glucose [Oroxylum indicum were associated with cancer, such as EGFR, PIM1, ESR1/2, and MAPK8/10. These genes are closely related to cell proliferation, differentiation, or migration and participant in pathways in cancer, complement and coagulation cascades, apoptosis, signaling pathway, and so on. More importantly, some of these are transcription factors that coregulate several pathways simultaneously, like EGFR and ESR1/2. Consistently, Li et al. have also discovered that O. indicum induces apoptosis via PI3K/Akt/PTEN signaling pathway in liver cancer [Oroxylum indicum exerts.Accordingly, we identified an inflammatory role for  glucose . These fr cancer . These iOroxylum indicum has multiple targets to exert multiple pharmacological effects. Multiple target therapeutic drugs are more effective for the treatment of complex diseases, such as cough, asthma, pertussis, and cancers, and are less vulnerable to drug resistance. Therefore, Oroxylum indicum could be a promising resource that may be used as a lead compound, chemical moiety, or active ingredients for further drug development. Nevertheless, we have to acknowledge that there are some limits and bias in our analyses due to the databases we employed, for example, the release, update date, and resources they used.The drug-targets network illustrated in Oroxylum indicum is a valuable TCM and contains quite promising compounds for the development of an effective and safe multitargeted anti-inflammatory and antitumor medicament. This study provides novel insight into the perspectives and challenges as for the Oroxylum indicum research and clinical application in future investigations.In short, we would like to emphasize that


Impulse control disorders (ICDs) in Parkinson's disease (PD) are aberrant behavior such as pathological gambling, hypersexuality, binge eating, and compulsive buying, which typically occur as a result of dopaminergic therapy. Numerous studies have focused on the broad spectrum of ICDs-related behaviors and their tremendous impact on patients and their family members. Recent advances have improved our understanding of ICDs. In this review, we discuss the epidemiology, pathogenesis and treatment of ICDs in the setting of PD. ChronicPG is defined as persistent and recurrent problematic gambling behavior as indicated by features such as increasing amounts of money, restlessness or irritability when cutting down, failing to control the behavior, preoccupation with gambling, lying to others, and so on (at least four criteria required). However, based on neuropsychiatric and possibly pathophysiological features, PG is currently considered as typical example of behavioral addiction and included in the diagnostic category of “substance-related and addictive disorders” according to DSM-5 , 7. HypeIn addition to the ICD, there are also some ICD-related disorders (ICRDs), such as Dopamine dysregulation syndrome (DDS) and punding. DDS implies repeated, unnecessary, or sometimes deleterious daily intake of dopaminergic agents far more than the dosage required for treatment of objective motor impairment, leading to severe dyskinesia, euphoria, aggressivity, hallucination, confusion, or frank psychosis . PundingBesides the classic ICDs symptoms there are many other ICDs-related behavioral problems including reckless driving , impulsiThis review mainly focuses on the ICDs in PD patients from the point of epidemiology, pathogenesis and therapeutic strategies.The prevalence of ICDs in PD patients using dopamine replacement therapy (DRT) varied from 3.5 to 43% –23. DopaICD is probably much more frequent in PD than previously reported as patients often underestimate the presence and severity of ICD symptoms . This ispost-hoc analysis about PD treated with rotigotine, although no definite conclusion can be reached on any dose-response relationship between rotigotine and ICDs, the incidence of ICDs appeared to increase with the dosage increase  (The duration of disease or medicine was correlated with ICDs in PD . Then th < 0.01) , remindiThere have been studies focusing on DNA polymorphisms of impulsive and compulsive behavior and additive behaviors in decades. A larger number of SNPs in dopaminergic  glutamatergic, serotonergic and opioid neurotransmitter system have been reported as candidates that improved predictability of ICDs when compared with clinical risk factors –56. We hA multicenter case-control study showed that specific subtypes of ICDs, such as compulsive shopping, binge eating and punding, had high frequency and were more severe in PD patients with Parkin mutation compared with non-Parkin mutation . The posComparing with single genetic variants, multiple gene interactions may play a more important role. Using candidate genetic multivariable pane, Kraemmer et al. conducted an interesting study to estimate ICD heritability in PD patients, which included several transmitter systems such as dopamine, serotonin, and norepinephrine genes. They found a substantial 11–16% increase in ICD behavior predictability compared to examining clinical variables alone. In addition, in 13 candidate variants, OPRK1, HTR2A and DDC genotypes were the strongest genetic predictive factors and OPRK1 polymorphism rs702764 significantly predicted incident of ICD behavior. Hence, they suggested the potential for developing clinical-genetic models to identify PD patients at increased risk of developing ICD and further guide treatments .Although polymorphisms of dopaminergic genes are not considered as the strongest risk factor for developing ICDs in PD patients at present, further research in the genetic susceptibility will explain the reason why some patients taking low doses of dopaminergic drugs still develop ICDs. Genetic studies can enlarge the understanding of ICDs from pathogenesis to therapy.Various neuropsychological studies have found that ICDs are associated with frontal/executive dysfunctions –70. ImbaHowever, there were several studies considering no difference in frontal executive dysfunction on neuropsychological testing between ICDs and non-ICDs patients in PD –78, whicIntertemporal choices, decisions between options available at different times, are commonly applied in impulsivity-related studies. The presence of impulsivity trait in intertemporal choices is usually suggested by a strong preference for small immediate rewards over large delayed ones . TemporaThe mechanism of ICDs is not well-understood. Yet based on animal and clinical researches, the dopaminergic system has been strongly implicated. There are three main dopaminergic pathways in central nervous systems (CNS): (a)the nigrostriatal pathway consisting of cell bodies in the substantia nigra (SN) whose axons terminate in the corpus striatum; (b) the mesocorticolimbic pathway , whose cell bodies are situated in the ventral tegmental area and whose axons project to parts of the limbic system; and (c) the tuberoinfundibular pathway, whose cell bodies are found in the ventral hypothalamus and project to the median eminence and pituitary gland . Of the Dopamine, as a modulator of risk behavior along the mesocorticolimbic pathway, plays an important role in reinforcement of learning. It signals the difference between predicted and experienced reward, and is also involved in shaping behavior to maximize reward and avoid punishment . NormallThere are several observations relevant to reward system and ICDs that are worth highlighting. First, the usual pleasurable stimuli such as food can induce tonic dopamine response in the outer shell of the nucleus accumbens. Second, dopamine auto-receptors in SN provide feedback to regulate synaptic dopamine concentration. Third, the orbitofrontal and anterior cingulate cortices (ACC) are involved in the top-down control, evaluating the reward and directing a suitable reaction, thus making adjustments for optimizing future choices . FinallyPD is associated with a neurodegenerative process that involves mesocorticolimbic network, but there is paucity of data linking abnormalities of this network to ICDs. One study, using volumetric magnetic resonance imaging (MRI) technique, found that amygdala volume was greater in PD patients with ICDs than those without ICDs, but similar to health control subjects . Since aThe mesocorticolimbic reward system plays an important role in the development and maintenance of addictive behavior. The reason for male preponderance in patients exhibiting this behavior is still unclear, but some imaging studies have suggested that males have a stronger functional connectivity in the reward mesocorticolimbic system than females , 92. ThuIn addition to the reward system, ICDs are also involved in the inhibition system. One fMRI study showed impairment in response-inhibition abilities in ICDs patients . The obsDopamine agonists improve motor symptoms in PD patients through their effects on the dorsal striatum, but they also activate the ventral striatum and the mesolimbic pathway . The braIn ICDs patients with PD, caregivers suffer huge burden from mental stress specifically on spousal safety. ICDs are associated with high rate of separation and divorce, child abuse, and neglect , 105. ItAs discussed above, ICDs are considered to have strong relationship with dopamine agonists. Decreasing or even withdrawing dopamine agonists is usually the first choice for clinicians. For PD patients who have developed ICDs on account of dopamine agonist treatment, they will get remission significantly after decreasing dosages. One longitudinal study suggested that ICDs resolved after 1 year in about 50% of the patients who stopped dopamine agonists and continued to improve . PatientConsidering the benefit from reducing dose of dopamine agonists such as pramipexole or ropinirole, it is recommended that temporary replacement of pramipexole by bromocriptine instead may relieve or reverse the ICDs while the D2 stimulation needed for motor symptoms are still maintained .Subthalamic nucleus (STN) deep brain stimulation (DBS) as a treatment for ICDs is considered a controversial method according to reported literature. Compared to patients without ICDs, ICDs patients exhibited increased proportion of subthalamic neurons responsive to prospective reward and decreased proportion to prospective loss in STN but no in GPi . STN-DBSAmantadine, acting as a dopaminergic and glutamatergic modulator, was reported to have great effect on reversing ICD symptoms without aggravating motor function. Amantadine add-on therapy is considered to reduce hypersensitivity in ICDs patients therefore decrease risky choice –127. HowAs ICDs are thought to be linked to oral dopamine agonists, strategies utilizing intrajejunal levodopa which utilize continuous drug delivery may decrease the risk of developing ICDs. This therapy may become a popular treatment of ICDs not only because its positive effect on behavioral disorders but also motor complications , 130.Clozapine were reported as a potential treatment for refractory ICDs. Clozapine not only has an effect on dopamine-blocking activity in the limbic system but also has weak antagonistic D3 and high antagonistic D4 activity that makes it capable of adjusting the reward circuit. Besides, N-desmethyloclozapine, the major active plasma metabolite of clozapine, may have an important partial agonist activity on dopamine D2/D3 receptors . There hThere has been an emerging method to treat ICDs using transcranial magnetic stimulation (TMS). A study reported that low-frequency repetitive TMS was used to treat four PD patients with punding whose symptoms were reversed magically. TMS deserves more studies to explore the best pattern and more indications .To date, there have been few studies concerning the role of cognitive behavior therapy in ICDs. Okai et al. proved the efficacy of cognitive behavior treatment in ICD patients with PD through a randomized controlled trail. They found the combined treatment of cognitive behavior treatment with medical care was more effective in reducing the severity of ICDs compared with medical care alone. The severity of symptom  index) significantly reduced 75% of the experimental group compared with only 29% in the control group. The intervention seemed to be also effective in depression and anxiety. Larger and long-term follow up studies are needed to confirm the benefit of cognitive behavior treatment in each subtype of ICDs, and meanwhile assess the cost-effectiveness .In addition to those treatments discussed above, valproate, zonisamide, naloxone, apomorphine, and bromocriptine may also be beneficial in treating ICDs , 137. DoIn conclusion, present studies remind us to pay much attention to non-motor symptoms including ICDs. With rapid advance regarding to ICDs, mechanisms of ICDs will become gradually clear and specific individual treatment strategies will be applied in the future. Given that ICDs would have terrible impact and consequence on families, patients and their caregivers should be educated in clinical practice. In addition, ICDs in PD patients may also provide a model for better understanding of the neurobiology of addiction.Y-CW provided the funding support and designed the project. J-FZ, X-XW, and YF searched for literature and wrote the manuscript. Y-CW, RF, and JJ revised the paper. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The article presents experimental tests of a new type of composite bar that has been used as shear reinforcement for concrete beams. In the case of shearing concrete beams reinforced with steel stirrups, according to the theory of plasticity, the plastic deformation of stirrups and stress redistribution in stirrups cut by a diagonal crack are permitted. Tensile composite reinforcement is characterized by linear-elastic behavior throughout the entire strength range. The most popular type of shear reinforcement is closed frame stirrups, and this type of Fiber Reinforced Polymer (FRP) shear reinforcement was the subject of research by other authors. In the case of FRP stirrups, rupture occurs rapidly without the shear reinforcement being able to redistribute stress. An attempt was made to introduce a quasi-plastic character into the mechanisms transferring shear by appropriately shaping the shear reinforcement. Experimental material tests covered the determination of the strength and deformability of straight Glass Fiber Reinforced Polymer (GFRP) bars and GFRP headed bars. Experimental studies of shear reinforced beams with GFRP stirrups and GFRP headed bars were carried out. This allowed a direct comparison of the shear behavior of beams reinforced with standard GFRP stirrups and a new type of shear reinforcement: GFRP headed bars. Experimental studies demonstrated that GFRP headed bars could be used as shear reinforcement in concrete beams. Unlike GFRP stirrups, these bars allow stress redistribution in bars cut by a diagonal crack. Corrosion of reinforcing steel is the main factor causing material degradation in reinforced concrete structures, which, if exposed to particularly adverse environmental conditions, cease to meet the requirements of durability and reliability in a facility in a relatively short time ,2,3,4. TBased on, among others, Fib Bulletin 40  and ACI Considering the above and the current state of knowledge reported in the literature, it should be stated that the behavior of support zones reinforced with FRP bars differs from those reinforced with steel. The limited number of studies on the subject does not permit explicit determination of the behavior of support zones reinforced with composite bars, especially support zones with low shear slenderness. Drzazga  indicatea/d ratio ≈ 1.7 < 2.5).The main purpose of the experimental research was to verify the failure modes of beams reinforced with GFRP stirrups and GFRP headed bars. Four beams with differently shaped longitudinal and transverse reinforcement were tested. All beams were loaded with forces concentrated at a distance of 600 mm from the support . It was The beams were designed as T-sections with a 400 mm section height, 100 mm flange height, 400 mm flange width, and 200 mm web width. The total length of the beams was 4300 mm. The elements differed in the type of longitudinal reinforcement and the type of shear reinforcement. Twenty millimeter steel bars and 20 mm GFRP bars were used as longitudinal tensile reinforcement. GFRP stirrups with a diameter of 12 mm and GFRP headed bars with a diameter of 12 mm were used as shear reinforcement. Each beam was designed with two differently reinforced support zones; the same type of reinforcement was used throughout the entire beam, but the spacing of stirrups and headed bars was differentiated. The beams were designed so that the shear zones had the same area of tensioned bars taken into account in dowel action. A high class of concrete was used (concrete compressive strength around 70 MPa), and the T-section was used in order to increase the flexure strength. The method of reinforcing research elements is shown in The beam testing was accompanied by the determination of the average compressive strength and the average modulus of elasticity of the concrete. The mechanical properties of the concrete were determined in cylindrical samples with a diameter of 150 mm and a height of 300 mm. The average compressive strength of the concrete for beams B-1, B-2, B-3, and B-4 was 68.94 MPa, 70.13 MPa, 65.47 MPa, and 71.39 MPa, respectively. The average modulus of the concrete elasticity under compression in the case of beams B-1, B-2, B-3, and B-4 was 35.47 GPa, 36.63 GPa, 35.97 GPa, and 38.07 GPa, respectively.As longitudinal reinforcement, steel bars with a diameter of 20 mm and a characteristic yield strength of 500 MPa and GFRP bars with a diameter of 20 mm and an average tensile strength > 1100 MPa, as declared by the manufacturer, were used. The modulus of elasticity of the steel bars and GFRP bars was 200 GPa and 57 GPa, respectively, as declared by the manufacturer. GFRP stirrups with a diameter of 12 mm and headed bars with a diameter of 12 mm were used as shear reinforcement.Both stirrups and headed bars were a Schöck product described as Schöck Combar. The bars used were made of E-CR  glass fiber composite with a diameter of approximately 20 μm and vinyl ester resin. The head anchorage was a fiber reinforced polymer mortar. The anchor head was 60 mm long, and the maximum diameter was 30 mm a. In ordfu, modulus of elasticity Ef, and ultimate strain εu were determined by the procedure in [Fu, the tensile capacity of FRP bar; A, the cross-sectional area of FRP bar; F1, the tensile load at approximately 50% of the ultimate load capacity; ε1, the tensile strain at approximately 50% of the ultimate load capacity; F2, the tensile load at approximately 20% of the ultimate load capacity; ε2, the tensile strain at approximately 20% of the ultimate load capacity.Tensile strength edure in  using thStraight bars were stretched in a testing machine using classic jaws used for the tensile test a, steel fbend = 770 MPa and the mean value of modulus of elasticity at E = 57 GPa. The stirrups used in this study were bent during manufacture, and the bending diameter was seven times the diameter of the bar (84 mm). The stirrups shaped in the same way were the subject of research by the manufacturer and Kurth in [Based on the data provided by the manufacturer and by Kurth , the aveKurth in . In thisKurth in . The examination of each beam consisted of two stages. In the first stage, the beam was subjected to a 4-point bending until one of the support zones was damaged . In the For the first stage, the load causing the appearance of diagonal and perpendicular cracks was determined. The angle of inclination of the compressed concrete strut was also established for each stage. The element was loaded until the appearance of cracks, and the load was then increased at a speed of about 0.2 kN/s, stopping at ca. every 50 kN; each time the crack pattern and the load at which it occurred was monitored. For each beam, strain measurements of concrete were made in the compression zone and the tension bars. Concrete strains were measured using RL300/50 resistivity strain gauges. Strain gauges were glued onto the concrete around the stirrups (on both sides of the web). Beam deflections were recorded in the middle of the span and under concentrated forces using inductive sensors. Displacement of supports was also measured using inductive sensors located next to the support points. The strains of steel bars and GFRP bars were measured with the use of RL120/20 strain gauges. Strain gauges on bars were placed in the middle of the length of the vertical arms of stirrups/bars with head anchoring. Strain gauges were also glued to longitudinal bars in the middle of the span and in cross-sections under applied forces. s = 69 MPa. The mean modulus of elasticity was 58.1 GPa, with a standard deviation of s = 3.4 GPa. The mean value of the ultimate strain was 22.4‰ with a standard deviation s = 0.7‰. The damaged sample after the test is shown in The failure mode of straight bars was of an explosive nature, and all bars were characterized by linear-elastic behavior over the entire strength range. The average tensile strength of a straight GFRP bar was 1299 MPa with a standard deviation s = 15 MPa. The average modulus of elasticity was 61.9 GPa, with a standard deviation of s = 3.0 GPa. The damaged sample after the test is shown in Headed bars were characterized by linear-elastic behavior until stress initiating the loss of bar adhesion to the anchor head was reached. The failure mode consisted of the slipping of the bar from the anchor head and was radically less violent than bar rupture. Vint tested the anchoring of GFRP headed bars in concrete and came to similar conclusions . A relatdf,i was calculated. Fifteen diameter measurements were made for each bar over a distance of 200 mm. The average value of the diameter df is given in fu, modulus of elasticity Ef, and ultimate strain εu were determined based on Formulas (1)–(3), and the results are shown in Before the test, the bar diameter was measured with an accuracy of 0.1 mm. The diameter was measured taking into account the ribs, then the thickness of the ribs was measured, and the core diameter of the bar cross-section The short-term tensile strength of headed bars was lower than that of straight bars. The strength of headed bars was about 42% of the tensile strength of straight bars. In addition, based on data from the manufacturer, it was found that the strength of headed bars constituted about 71% of the strength of the bent stirrup section c. The moEach beam was tested in two stages in order to obtain results from eight support zones. The subject of the analysis was both the general behavior of concrete elements reinforced with composite bars and the shear issue, which was the main subject of this study. This article presents the results in terms of bearing capacity and failure mode, static equilibrium paths, and the strains of the reinforcement of the support zone. Complete results of leading and accompanying studies were presented by Drzazga in . All beams were designed in order to obtain the failure in the support zone. All the support zones were damaged as a result of the failure of the transverse reinforcement cut through a diagonal crack (shear–tension failure) .The failure of the support zones reinforced with GFRP stirrups was of a violent nature and occurred with the rupture of the stirrup near the bend a. The suThe compaction of shear reinforcement, from 220 mm to 160 mm, resulted for the B-1, B-2, B-3, and B-4 beams in increasing the load capacity by approximately 6%, 20%, 35%, and 15%, respectively. The increase in load capacity was clearly greater for beams with longitudinal composite reinforcement. It can also be observed that using a larger number of bars for shear stress transfer, which being cut by a diagonal crack accounted for the contribution of transverse reinforcement in shear capacity, resulted in a greater increase in load capacity for support zones reinforced with headed bars than with stirrups. Along with the increase in load, an increase in the strains of headed bars was recorded up to the initiation of the slipping of the bars (furthest from the top of the crack) out of their anchor heads. Along with the slipping of the bar out of the anchor head, the level of strains on this bar stabilized and the stress was redistributed, which allowed for a more regular distribution of headed bars’ strains than in the case of stirrups. s = 220 mm and s = 160 mm, was about 25% and 12%, respectively.Support zones reinforced with GFRP stirrups were characterized by a higher load carrying capacity than those reinforced with GFRP headed bars. The exception were the support zones B-1_160 and B-4_160, where the support zone with headed bars had a carrying capacity of about 2% higher. This was caused, among other things, by the greater ability of headed bars to redistribute strains (destructive crack cut two pairs of bars). In the case of longitudinal steel reinforcement and shear reinforcement spaced 220 mm, the load capacity of the supporting zone reinforced with stirrups was greater by about 6% than that reinforced with headed bars. Similar values of bearing capacity of support zones longitudinally reinforced with steel bars may result from the high axial stiffness of longitudinal reinforcement. As a consequence, a substantial part of the shear capacity was the bearing capacity of concrete and longitudinal reinforcement. In the case of longitudinal GFRP reinforcement, this increase, for the spacing The deflection of the research elements was determined on the basis of the results recorded by five induction sensors with an accuracy of 0.001 mm. Two sensors were located at the supporting plates, two directly under the forces and one in the middle of the span. Recording of displacement values took place continuously.Longitudinally reinforced beams with fiber glass bars (B-2 and B-3) were characterized by higher deflection values at individual load levels than longitudinally reinforced beams with steel bars (B-1 and B-4). This was due to the fact that in the case of beams with longitudinal steel reinforcement, five longitudinal bars were used in the span zone. In addition, the greater axial stiffness of steel reinforcement (as compared to GFRP) resulted in greater bending stiffness of the reinforced concrete element, which was particularly evident in the cracked phase, where the stiffness of an element was largely determined by the modulus of elasticity of longitudinal reinforcement, and this modulus was more than three times smaller for GFRP bars than for steel bars.w was recorded in the middle of the span, while for Stage 2, under concentrated force.In In the course of the tests, the strains of stirrups and headed bars were recorded continuously. Strains were recorded in the central part of the vertical straight segment of stirrups and in the middle of headed bars. The test results were presented in the form of shear reinforcement strains for different load levels together with the crack pattern of the support zone. The strain values given were the average of two vertical straight sections of stirrups or headed bars. In the case of support zones reinforced with GFRP stirrups, along with the increase in load, the increase in stirrup strains was recorded, almost proportionally, until failure. Strains of headed bars increased almost in proportion until strains initiating the loss of bar adhesion to the anchor head were reached. If one pair of headed bars was cut, failure occurred as a result of the slipping of the bar out of the anchor head. If two pairs of bars were cut, along with an increase in load, an increase in the strains of bars with head anchoring was recorded up to the initiation of the slipping of the bars (furthest from the top of the crack) out of the anchor heads. Along with the slipping of the bar from the anchor head, the level of strains on this bar stabilized, and stress was redistributed, which allowed a more regular distribution of strains in the case of headed bars than in the case of stirrups.For the B-1 beam, the maximum strains recorded on the straight section of stirrups were 5.15‰ (B-1_220) and 5.37‰ (B-1_160). For the B-2 beam, the maximum strains recorded on the straight section of stirrups were 9.10‰ (B-2_220) and 8.07‰ (B-2_160). The difference in the maximum strains recorded may be due to the fact that the strain gauges were glued to the middle of the vertical sections of stirrups. In the case of B-1_220 and B-1_160, the destructive diagonal crack cut the stirrup slightly above the bend. In the case of B-2_220 and B-2_160, the destructive diagonal crack cut the stirrup much closer to the central part of the vertical sections.For the B-3 beam, the maximum strains recorded on bars with head anchoring were 4.87‰ (B-3_220) and 3.08‰ (B-3_160). For the B-4 beam, the maximum strains recorded on the headed bars were 5.85‰ (B-4_220) and 2.64‰ (B-4_160). Maximum strains in the case of the more heavily reinforced support zone were much smaller than in the case of the less heavily reinforced support zone, as during the tests on a less heavily reinforced support zone, the bars of a more heavily reinforced support zone were also deformed. As a result of the load in Stage 1, the process of bars slipping from anchor heads was initiated.The article presented experimental tests of a new type of composite bar that was used as shear reinforcement for concrete beams. The results of the experimental studies of bar tests and short support zones reinforced with GFRP stirrups or GFRP headed bars were presented. On the basis of the experimental studies, the following final conclusions were formulated:(1) The failure mode of straight bars Φ12 GFRP (Schöck Combar) had an explosive character, and all bars were characterized by linear-elastic behavior over the entire strength range. Based on the manufacturer’s data and research, including Kurth’s research , it was (2) GFRP headed bars could be used as shear reinforcement. The failure mode of under reinforced support zones consisted in the bar slipping out of the anchor head.(3) The immediate force initiating the failure of a GFRP headed bar was smaller than the strength of the bent fragment of GFRP stirrups. In the case of a support zone reinforced with headed bars, a more regular distribution of strains could be expected than in the case of analogous reinforcement in the form of frame stirrups, which was confirmed by tests on support zones of beams in a natural scale.(4) All eight support zones were damaged as a result of the failure of the transverse reinforcement cut by a diagonal crack. The failure of support zones reinforced with GFRP stirrups was violent in its nature and occurred along with the rupture of the stirrup near the bend. Support zones reinforced with headed bars had a less violent mode of failure.(5) The change of shear reinforcement spacing from 220 mm to 160 mm resulted in an increase in load bearing capacity. The increase in load bearing capacity was clearly greater for beams with longitudinal composite reinforcement. In addition, a greater increase in load bearing capacity was observed for shear zones reinforced with headed bars than for shear zones reinforced with stirrups. Using a larger number of bars cut by a diagonal crack (the contribution of transverse reinforcement in shear capacity) resulted in a greater increase in load bearing capacity for support zones reinforced with headed bars than with stirrups, which indicated a more regular distribution of stress on individual bars cut by a diagonal crack.(6) The presented research was a new contribution to the experimental study of support zones reinforced with FRP bars. GFRP headed bars could be an alternative for GFRP stirrups and could be used in concrete elements in facilities particularly exposed to aggressive environments and in facilities whose proper functioning is dependent, among other things, on the electromagnetic neutrality of the construction elements. Nevertheless, continuing research in the field of shear behavior of concrete beams reinforced with different type of FRP reinforcement is necessary, both support zones with low and high shear slenderness.


This study aims to study the material basis and effective mechanism of musk for ischemic stroke (IS) based on the network pharmacology approach.  We collected the chemical components and target gene of musk from the BATMAN-TCM analytical platform and identified ischemic stroke-related targets from the following databases: DisGeNET, NCBI-Gene, HPO, OMIM, DrugBank, and TTD. The targets of musk and IS were uploaded to the String database to construct the protein-protein interaction (PPI) network, and then, the key targets were analyzed by topological methods. At last, the function biological process and signaling pathways of key targets were carried out by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and cluster analysis by using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) server and Metascape platform.  A total of 29 active compounds involving 1081 predicted targets were identified in musk and there were 1104 IS-related targets. And 88 key targets of musk for IS were obtained including AKT1, MAPK1/3, TP53, TNF, SRC, FOS, CASP3, JUN, NOS3, and IL1B. The GO and KEGG enrichment analysis suggested that these key targets are mainly involved in multiple pathways which participated in TNF signaling pathway, estrogen signaling pathway, prolactin signaling pathway, neurotrophin signaling pathway, T-cell receptor signaling pathway, cAMP signaling pathway, FoxO signaling pathway, and HIF1 signaling pathway.  This study revealed that the effective mechanisms of musk against IS would be associated with the regulation of apoptosis, inflammatory response, and gene transcription. Stroke, an acute cerebrovascular event caused by brain arterial or venous thrombosis or intracerebral hemorrhage, presents a variety of clinical syndromes such as facial paralysis, skewed mouth, slurred speech, or paralysis . The deaMoschus, Chinese pinyin: Shexiang) was first set out in Shen Nong's Materia Medica and could treat stroke because of the effect of inducing resuscitation, activating blood, dissipating stagnation, and alleviating pain [The Guide recorded that musk could clear orifices from our muscles into the bone marrow. Compendium of Materia Medica says that it can open contained orifices, channels, and collaterals [Chinese herbal medicine has treated stroke for a long time and could provide a rosy prospect for stroke because of rich drugs and prescription resources , 7. Musking pain . The Guilaterals , 10. Prelaterals  and showlaterals . Therefolaterals . HoweverHopkins first set out that “network pharmacology has become a new scientific method that combined biology, pharmacology, bioinformatics, and computer technology to systematically study the effective mechanisms of multicomponent and multitarget drugs” . This meNetwork pharmacology mainly includes data acquisition and network analysis. The data obtained includes active ingredients of traditional Chinese medicine, a target of component action, and a target of disease-related genes. Based on the data obtained, network analysis constructs and analyzes the interaction between the nodes of the network, the network topology characteristics, and the identification of biological processes and signal pathways affected by the target, thereby clarifying the mechanism of action of traditional Chinese medicine. The following is the role of the database used in the study, as well as the flow chart  database to search for the predicted target, selected the species as “Homo sapiens,” and excluded nonhuman sources and nonstandard targets. Finally, the relevant targets of musk active ingredients were obtained.In this study, the BATMAN-TCM analytical platform was used to obtain the composition of musk and to predict the target of the composition. BATMAN-TCM is an online analysis platform for the Molecular Mechanism of Traditional Chinese Medicine. It mainly performs target prediction, target gene function analysis, pathway enrichment analysis, and multitraditional Chinese medicine comparative analysis . It useshttp://www.disgenet.org) [https://www.ncbi.nlm.nih.gov/gene/), (3) Human Phenotype Ontology (HPO) (https://hpo.jax.org/app/) [https://www.omim.org/) [https://www.drugbank.ca/) [http://bidd.nus.edu.sg/group/cjttd/) [Ischemic stroke-related targets are collected from the following six disease-related databases: (1) DisGeNET (net.org) , (2) NCBrg/app/) , (4) Onlim.org/) , (5) Druank.ca/) , and (6)/cjttd/) . In theshttps://String-db.org/) database is a database used to search for protein interactions. It contains a large number of known or predicted protein-protein interactions, including both direct physical interactions and indirect functional relationships [http://bioinfogp.cnb.csic.es/tools/Venny/index.html) was used to analyze the intersection of musk component target and ischemic stroke target PPI. Based on the results of the PPI network intersection, the candidate PPI network was determined, and the target core PPI network of musk-ischemic stroke was further screened through the network topology characteristics.String (ionships . Targetshttp://www.cytoscape.org/). Cytoscape is an open-source software, which conveniently offers numerous bioinformatic analysis tools to further network analysis.The following networks were constructed in this study: (1) compound-compound target network of musk, (2) compound target-ischemic stroke network, and (3) a complex network of “compound-target-pathway”. All the networks were generated by employing the network visualization software Cytoscape  provides detailed annotation of genes from cell components, molecular functions, and biological processes [https://www.kegg.jp/) provides integration of chemical, genomic, and systemic functional information for understanding the advanced functions and uses of biological systems at the molecular level [http://www.david.niaid.nih.gov) integrates GO, KEGG, UniProt, and DrugBank, among other authoritative data sources, to provide systematic bioinformatic annotations for large-scale genes or proteins, including annotation of biological processes and enrichment of pathways [P value, the core targets were displayed in the form of a chart.Gene Ontology  [In complex biological information networks, some genes or proteins are closely related and have the same or similar functions, so they can be regarded as a cluster and play an important biological role in coordination. The information of each node in the network helps us to perform cluster analysis and build functional modules . Moleculpe.org/) .According to the BATMAN-TCM database, we selected the chemical composition and target information of musk with the Score cutoff value greater than 20. Among them, 29 chemical constituents of musk were obtained, including macrocyclic ketone compounds such as muscone and normuscone, steroid compounds such as androst-4-ene-3, 17-dione, muscol, and 17-beta-estradiol, and pyridine compounds such as muscopyridine and hydroxymuscopyridine a/b , Rap1 signaling pathway, and so on  and cluster 2 (MCODE 2) was carried out. Top 10 KEGG enrichment pathways were obtained and framed in a bubble plot according to the P value . As depiIntegrating the results of network pharmacological analysis, we constructed a multidimensional network of “traditional Chinese medicine-composition-target-pathway,” as shown in According to TCM theory, ischemic stroke belongs to the category “apoplexy” and causes sudden faint flutter, deviation of the eye and mouth, hemiplegia, etc. Cerebral blood stasis was basic pathogenesis. Therefore, clinical treatments are mainly based on inducing resuscitation, promoting blood circulation by removing blood stasis, and the effect is significant. Chinese medicine “musk” has resuscitation, promoting blood, dissipating mass, and analgesic effect, commonly used in the treatment of ischemic stroke. Currently, basic and clinical research on musk for ischemic stroke mostly focuses on a single component or a single pharmacological effect. However, the pathophysiology of ischemic stroke is complicated. Musk plays a role in the treatment of ischemic stroke through multiple links and multiple ways, and those studies cannot fully reflect that. Studies on system biology have revealed a high degree of correlation between molecular network regulation and diseases. Especially for complex systemic diseases, the simultaneous intervention of multiple targets may achieve better efficacy and less toxic and side effects. Network pharmacology is based on the “disease-gene-target-drug” interaction network, which systematically observes the intervention and impact of drugs on disease networks, thereby revealing the mystery of the synergistic effect of drugs on the human body . Taking In the current study, a total of 29 ingredients including macrocyclic ketone compounds such as muscone, muscol, and normuscone, steroid compounds such as androst-4-ene-3, 17-dione, testosterone, and 17-beta-estradiol, pyridine compounds such as muscopyridine and hydroxymuscopyridine a/b were identified as potential active ingredients of musk. Some active ingredients have proved to be effective in reducing brain damage and improving neurological function after ischemic stroke. For example, muskone can inhibit the expression of Fas and caspase-8 in the cortex, thereby inhibiting apoptosis of neural cells and significantly reducing the volume of cerebral infarction . In addiAfter analyzing the PPI network of musk for ischemic stroke, we found that the core targets of musk for ischemic stroke were AKT1, MAPK1/3, TP53, TNF, SRC, FOS, CASP3, JUN, NOS3, IL1B, and so on. These targets were involved in transcriptional regulation, gene regulation, apoptosis regulation, and other biological processes. And the results of our research were consistent with the published studies.AKT is a serine/threonine protein kinase, which plays an antiapoptotic role by phosphorylating downstream target proteins . There aβ and TNF-α by activating the MAPK cascade [At present, MAPK signaling has been extensively studied, and it plays a pivotal role in regulating apoptosis and inflammatory cytokine expression after ischemic stroke . In the  cascade . TherefoFurthermore, TP53 is a tumor suppressor gene, which is associated with the regulation of cell cycle and apoptosis. When an ischemic stroke occurs, the brain is in ischemia and hypoxic environment, which makes the cells in a stress state, and DNA homeostasis is destroyed . Then, TBased on the KEGG enrichment analysis, musk was thought to influence some important pathways, which were highlighted to play important roles in anti-ischemic stroke. Those signaling pathways included the TNF signaling pathway, estrogen signaling pathway, prolactin signaling pathway, neurotrophin signaling pathway, T-cell receptor signaling pathway, cAMP signaling pathway, FoxO signaling pathway, and HIF-1 signaling pathway.α is significantly increased in patients with acute ischemic stroke. This suggests that TNF-α plays a crucial part in the pathogenesis of ischemic stroke [α affects blood-brain barrier permeability. It can activate glial cells and mediates the expression of adhesion molecules on vascular endothelial cells, so as to promote infiltration of neutrophils [α has potential application prospects in reducing blood-brain barrier disruption and improving neurological prognosis [α in patients with ischemic stroke and improve the effectiveness of treatment [The pathophysiology of ischemic stroke is very complicated. And inflammation and immune response are important pathophysiological changes in the progression of ischemic stroke, which involves both innate and adaptive immune systems. After a stroke, damaged nerve cells will induce glial cell activation, peripheral immune cell infiltration, and release of inflammatory mediators. Then, the damage of the blood-brain barrier and the occurrence of cerebral edema is aggravated, causing secondary brain damage . Lee et c stroke . TNF-α atrophils . Consequrognosis . Clinicareatment . Moreovereatment . Althougreatment . And, a reatment .β-estradiol, is one of the main ingredients in estrogen. Evidence showed that 17-β-estradiol can reduce the infarct volume and reperfusion injury after stroke and accelerates the recovery of motor and sensory functions [Estrogen mediates genetic effects by binding to the receptors on the nucleus or cell membrane . In receunctions . Moreoveunctions . Hyperprunctions . Neurotrunctions . In animunctions . This inunctions . Hypoxiaunctions . TherefoThe ischemic stroke involves multiple pathological processes. Traditional Chinese medicine, musk, exerts its pharmacological effect on ischemic stroke through a “multicomponent, multitarget, multipath” effect mechanism. However, our research may have a few limitations. Further biological research is needed in order to verify the results of the above studies.β-estradiol, and musk pyridine have been discovered in this study. These components may have a neuroprotective role after ischemic stroke by regulating various pathways such as apoptosis, inflammatory response, and gene transcription. Our study explained the improvement effect of musk for ischemic stroke theoretically and helped the development and application of musk for ischemic stroke.In summary, based on the network pharmacology approach, we explored the material basis and effective mechanism of musk for ischemic stroke. Potential functional ingredients such as muscone, testosterone, 17-


Computational approaches to predict drug sensitivity can promote precision anticancer therapeutics. Generalizable and explainable models are of critical importance for translation to guide personalized treatment and are often overlooked in favor of prediction performance. Here, we propose PathDSP: a pathway-based model for drug sensitivity prediction that integrates chemical structure information with enrichment of cancer signaling pathways across drug-associated genes, gene expression, mutation and copy number variation data to predict drug response on the Genomics of Drug Sensitivity in Cancer dataset. Using a deep neural network, we outperform state-of-the-art deep learning models, while demonstrating good generalizability a separate dataset of the Cancer Cell Line Encyclopedia as well as provide explainable results, demonstrated through case studies that are in line with current knowledge. Additionally, our pathway-based model achieved a good performance when predicting unseen drugs and cells, with potential utility for drug development and for guiding individualized medicine. The use of machine learning algorithms has significantly progressed in predicting drug response by integrating genetic features and chemical structure information. Menden et al. derived multiple features from cell lines, including microsatellite instability status, mutations and copy number variations to predict drug response, and demonstrating that that chemical information improved significantly the drug models2. Another work, by Wang et al., used matrix factorization to predict drug response from low dimensional drug similarity space and cell line similarity space3 while a work by Liu et al. performed better by including drug response similarity4. Recently, Li et al.5 published a method called DeepDSC that predicts drug response by encoding gene expression features through an autoencoder and feeding the encodings together with chemical structure information into a deep neural network.Tailoring drugs to patients based on their genomic and environmental factors is one of the ultimate goals of precision medicine. Within precision oncology, using the molecular signatures of targeted genes has been useful for targeted therapy6 built a Bayesian model for inferring the relation between drug target proteins and cancer signaling pathway activities. This work assumed that if a drug pathway is activated in a tumor, then the tumor cells are likely to be sensitive to drugs that target genes in that pathway. Mathematically, the drug response variable  is factorized into drug target feature and signaling pathway feature. While gaining interpretability, this approach displayed low performance.While these computational models attempt to improve performance, the focus on explainable and generalizable models remains limited. Being able to explain the model results to oncology researchers can diminish a significant barrier for translation of prediction models to clinical setting of precision oncology. In order to address this barrier, a study by Yang et al.7 with cell-based pathway enrichment scores in these pathways. Testing our model on the Genomics of Drug Sensitivity in Cancer (GDSC)8 and the Cancer Cell Line Encyclopedia (CCLE) databases9, we demonstrate better performance than previously published deep learning approaches while case studies show that pathway features agree with current knowledge on drugs’ mechanism of action. To the best of our knowledge, this is the first pathway-based deep neural network for drug sensitivity prediction, and it provides a flexible framework to incorporate additional pathways using prior knowledge. Demonstrating the generalizability of our approach across databases and for predicting response to new drugs and cell lines, our approach can thus be useful both for drug development and for guiding individualized medicine.In this study, we integrated ideas of cancer pathways with deep learning constructs to develop a pathway-based model for the prediction of drug sensitivity in cancer, called PathDSP, which both performs well and is also explainable. The rationale is that drugs exert their therapeutic effects by affecting target proteins, further signaling downstream pathways. Activation of signaling pathways thus may indicate whether cells are sensitive or resistant to a drug when the activated signaling pathways are important for cell growth or death. We integrated drug-based pathway enrichment scores across 196 cancer pathwaysWe designed PathDSP for predicting drug response in cancer cell lines based on the rationale that cancer pathways would represent well drug therapeutic effects Fig. . We pred10, XGBoost11, Random Forest12, Support Vector Machine (SVM)13, and the fully connected neural network (FNN). We use two metrics to assess the performance, mean absolute error (MAE), which is less sensitive to outliers and the root mean square error (RMSE). As our error distribution is Laplacian , MAE is more appropriate but in order to compare to previously published methods, we need the RMSE and per Chai and Draxler paper14, a combination of the metrics are often required to assess model performance. The result of tenfold cross validation demonstrated that FNN obtained the best performance with an MAE of 0.24 ± 0.02 and RMSE of 0.35 ± 0.02 (Table S1). Therefore, we used the FNN model throughout the study.We compared the performance of six machine learning algorithms, including ElasticNet, CatBoostFor the FNN model, we further compared performance of subsets of the data types. Unsurprisingly, the combination of all data types obtained the lowest MAE of 0.24 ± 0.02 and RMSE of 0.35 ± 0.02 Table . Using e15. We tested CES profiles in the CCLE database as it shares more drug-cell line pairs with the CES dataset than GDSC (6491 pairs vs. 2803 pairs with GDSC). Our results show that MAE remained the same (MAE = 0.22) with a minor improvement in RMSE performance (RMSEs of 0.39 vs. 0.4 without CES). Lastly, it was infeasible to perform cross-validation test for the integration of drug expression profiles from the LINCS L1000 drug-induced expression profiles16 due to the low overlap between the drug-cell line sets in L1000 and either GDSC or CCLE (44 drug-cell line pairs shared with GDSC and 108 drug-cell line pairs shared with CCLE). We thus used the best performing model (without drug class information and CES) for the rest of the analysis.We tested also whether the addition of external knowledge data would improve the prediction. We first examined whether adding drug class information available in GDSC as one-hot matrix improves the performance. The result showed instead a decrease in performance , suggesting drug class information may overfit the model. We next tested the effect of adding combined gene essentiality scoring (CES) developed to integrate to integrate CRISPR and shRNA gene essentiality profiles with the molecular features of cancer cells5. All models used the same drug response and provided only RMSE on the GDSC dataset. PathDSP outperformed these four models, where the next best performer was DeepDSC with an RMSE of 0.52 (8.5 standard deviations from our results), followed by other models with RMSE between 0.83 and 1.43  and leave-one-cell-out (LOCO) by removing one drug or cell line, respectively, from training and assessing the prediction error for that missing drug or cell line (Methods). We obtained an average MAE of 0.83 ± 0.58 (RMSE of 0.98 ± 0.62) from LODO, and an average MAE of 0.45 ± 0.15 (RMSE of 0.59 ± 0.17) from LOCO. In particular, our LODO model obtained lower RMSE than DeepDSC, the only model that performed leave-one-drug-out out of the four previously compared models (1.24 ± 0.74).Hemopoietic and solid malignancies have significantly different clinical treatments. We thus tested the performance of the model on hemopoietic cell lines (21% of the cell lines in GDSC) and solid cancer cell line separately. Cross-validation performance was similar across cell line types . This result is much better performance than when training only on each group separately . Conversely, in our leave-one-cell-out experiment, the performance for hemopoietic cancer cells was better (RMSE = 0.54 ± 0.13) than for solid cancer cell lines (0.60 ± 0.18).9. PathDSP obtained an MAE of 0.93 (and RMSE of 1.15) when tested on the entire dataset of CCLE, with an MAE of 0.94 (RMSE of 1.16) when tested on drug-cell line pairs that to do exist in GDSC and an MAE of 0.74 (and RMSE of 0.95) when tested only on the set of drug-cell line pairs shared between GDSC and CCLE. Notably, the difference in the drug response measurements between the two datasets makes this task challenging. While GDSC tested a large range of IC50 values, CCLE capped their tested range at a concentration of 8 μM18, resulting in different response values of the same drug-cell line combinations across the two datasets .We computed Shapley values to identify important features underlying predictions. We focus on features that have positive contributions to drug response (i.e. the feature contributes to reducing IC50). The top features are distributed across the different data types, including expression, DG-Net, CNV and mutation, supporting our observation that the combination of these data types enables the good performance (figure S1). We highlight a subset of these globally important top features : The top feature is an enrichment of cell line expressed genes within the ADP-Ribosylation factor 3 (ARF3) pathway. The main gene of this pathway, ARF3 gene, is up-regulated in breast cancer and promotes breast cancer cell proliferation, representing a novel prognostic marker and therapeutic target for breast cancer20. Several genes within this pathway have established role in cancer, such as TP5322 and SIRT1, which is up-regulated in cancer cells and may play a critical role in tumor initiation, progression, and drug resistance by blocking senescence and apoptosis, and promoting cell growth and angiogenesis. SIRT1 inhibitors have shown promising anticancer effects in animal models of cancer23. The third top feature is CNV of Glypican 1 pathway, further discussed in the leave-one-cell-out specific example below. Finally, the next two features involve enrichment of the Nuclear factor kappa B (NF-κB) canonical pathway within mutation data and enrichment of Fanconi Anemia pathway by DG-Net. Indeed, mutations in the component of the core NF-κB signaling pathway have been implicated with relation to cancer24 and patients with Fanconi Anemia have a higher risk of cancer, particularly for acute myeloid leukemia and squamous cell carcinoma followed by ongoing work to study targeting of pathways that are synthetic lethal with loss of Fanconi Anemia pathway with Olaparib or other drugs25.The second top feature is enrichment of the Histone deacetylase III (HDAC-III) pathway within the drug-associated gene network (DG-Net). HDAC inhibitors seems to be promising anti-cancer drugsNext, we highlight two local feature importance predictions, one for the LODO (RVX208) and one for the LOCO scenario, demonstrating the drug- and cell line- specific explanability of PathDSP.26. The Activin receptor-like kinase-1 (ALK1) pathway ranked highest out of the features with positive contribution to drug responses . ALK1 is a receptor of TGF-beta type 1 receptor family and can regulate angiogenesis, which plays a critical role in the growth of cancer because solid tumors need a blood supply if they are to grow in size and tumors can actually cause this blood supply to form by stimulating angiogenesis27. Correspondingly, ALK1 is a well-known cancer driver which could act as a tumor suppressor or oncogene, depending on the cancer type, cell type, or ligand involved28 and is an emerging target for antiangiogenic therapy of cancer29. Additional genes in the ALK1 pathway include mitogen-activated protein kinase 1 and 3 (MAPK1 and MAPK3), where the suppression of BET inhibits vascular inflammation by blocking MAPK activation30.The best performing prediction of LODO is for RVX-208 drug . RVX-208 is a Bromodomain and Extra-Terminal domain (BET) inhibitor. BET regulates the transcriptional program and plays a role in influencing cancer pathogenesis and inflammation31, Canonical Wnt32, retinoic acid33 and bone morphogenetic proteins (BMP)34 pathways. One way in which these pathways connect to the ALK1 pathway is via shared member genes, including the MAPK1 and MAPK3 genes that are part of both the ALK1 and the retinoic acid receptors-mediated signaling pathways; and the SMAD1/4/5 genes included in the ALK1, ALK2 and BMP pathways, where in endothelial cells ALK1 activates the Smad1/Smad5 pathway35. Another way is through cross-talk between the pathways, as in the case of canonical Wnt signaling that was found to skew TGF-β signaling in chondrocytes via the ALK1 pathway36.Additional top features for RVX-208 are other pathways involved in angiogenesis, including ALK2th) pathways. Notably, JQ-1 was only tested on breast cancer cells, while the other three BET inhibitors were tested on multiple cancer cell types, which could explain the differences in the extracted top features.There are three other BET inhibitors in the GDSC dataset: I-BET-762, OTX015 and JQ1. In two of the BET inhibitors, I-BET-762 and OTX015, ALK1 is also one of the top features (rank 1st in I-BET-762 and 4th in OTX015). Additionally, some of the other above mentioned pathways, including the BMP pathway (ranks 4th and 6th), the ALK2 pathway (ranks 9th and 8th) and the retinoic acid pathway only in OTX015 (rank 10). The remaining BET inhibitor in our set, JQ-1, does not display the ALK1 pathway as a top feature but displays the Canonical Wnt signaling (rank 4th) and retinoic acid  that have been implicated with regard to activation in CML41, SRC Proto-Oncogene, Non-Receptor Tyrosine Kinase (SRC) that appears in both GPC-1 and syndecan-3 pathways, whose overexpression have been identified among the known mechanisms of resistance to imatinib in CML42 and FYN proto-oncogene, Src family tyrosine kinase that is up-regulated in CML as result of the BCR-ABL1 oncogene43. We note that our data includes additional five CML cell lines. Out of these five, three cell lines also have GPC-1 as one of the top important features . The other two CML cells show top pathways that are related to GPC-1, including bone morphogenetic proteins (BMP), mitogen-activated protein kinase (MAPK) and Wnt pathways. Glypicans are known to regulate BMP signaling44 and specifically GPC-1 and BMP expression are correlated in certain cancers like adenocarcinoma45, and mitogen-activated protein kinase (MAPK), where GPC-1 binds growth factors to facilitate their assembly for enhanced signaling in MAPK, among others46 and knockdown of GPC-1 decreased growth of ESCC cells and induced apoptosis via inhibition of MAPK signaling pathways in vitro47. Finally, altering GPC-1 levels modulates canonical Wnt signaling during trigeminal placode development and an in vivo role for glypicans has been demonstrated in association with Wnt signaling48.Our model obtained good performance when predicting drug response for the Chronic Myelogenous Leukemia (CML) cell line (SIDM00482) in the LOCO (MAE = 0.23 and RMSE = 0.31). Based on the Shapley values, gene expression enrichment of the SYNDECAN 3 pathway and copy number variations enrichment of the Glypican 1 (GPC-1) pathway display positive contribution to drug response . Syndecans and glypicans are membrane-bound heparan sulphate proteoglycans (HSPGs), they act as receptors and relate to cell growth and differentiation by interacting with other growth factorsThese two examples demonstrate that identifying cancer pathways association with drug-associated genes can help identify potential mechanisms and possibly new targets within the pathway.49.In this study, we presented PathDSP, a method that integrates drug and cell line information to predict drug responses of cancer cell lines. We addressed the high dimensional and heterogeneous nature of the data used in this task by mapping it to curated pathways. By using this approach, we gained improved performance over current state-of-the-art methods, improved generalizability of the models across independent datasets and provided a cancer pathway-level of explainability beyond that of the single gene or single mutation level. While we focused on manually curated cancer pathways and on specific genomics data including gene expression, mutation and copy number variation data, our framework is flexible and can seamlessly integrate additional pathways as well as other data types. We predicted IC50 values instead of reframing the problem as a binary classification problem since there is no agreed-upon threshold to determine sensitive vs. resistant cell line, owing in part to being drug- and cell-dependentWe demonstrated that integrating multiple types of data improve performance of the algorithm. Interestingly, for drug-related data types, relying only on engineered features from the drug-gene-network without chemical structure information obtained the worst performance, but the combination of the two improved performances over only one of them. Similarly, with regard to cell line specific data types, each of the individual cell-line data, i.e. gene expression, mutation and copy number variation, gain similar but reduced performance relative to their combination. Nevertheless, the reduction in performance in this case is smaller, holding a promise that our method could be applied even within clinical settings with only a subset of the data types measured. While external data, such as gene essentiality score (CES) or the L1000 gene expression profiles for drugs holds promise to further improve the models and their biological interpretation, the current low overlap between these datasets and the drug sensitivity datasets of GDSC and CCLE limits the integration of these datasets.The incompleteness of drug target data poses a potential limitation on good performance of PathDSP. In order to address that, we expanded the list of drug targets to include drug-associated genes from curated external databases and additional functionally-related genes by searching close neighbors of drug target genes within a protein–protein interaction network, potentially capturing also cross-talking pathways. As we demonstrate, the differences in response value measurements (IC50) across databases pose a limitation on the generalizability of methods designed to predict the response value, including PathDSP. However, we managed to improve the generalizability beyond the expected theoretical difference between the datasets , demonstrating good generalization capabilities, which would be critical in translating this method into clinical setting.Last, while pathway activity may be helpful in predicting drug response differences, biological pathway usually consists of several genes responsible for diverse functionality. Thus, more fine-tuned, drug and cell-line specific, experiments are necessary in order to pinpoint which gene(s) may be the ones most associated with a drug’s sensitivity or resistance. Additionally, while the explainable associations are good predictors, they are not necessarily causal, a relationship that would require additional experiments to establish.Our developed method of pathway-based deep neural network for drug sensitivity prediction demonstrated improved performance, generalizability and exemplified explainability. Given the flexibility of this approach, we believe it provides another means in which the roles of pathways in drug response for cancers can be evaluated and to provide another steppingstone towards cancer precision medicine.https://www.cancerrxgene.org/downloads/bulk_download.) Drug sensitivity data of 24 drugs and 478 cancer cell lines from the Cancer Cell Line Encyclopedia (CCLE) were downloaded through the DepMap portal , which also include gene expression, mutation and copy number variation data for those cancer cell lines. Primary target data was downloaded from GDSC, and PID pathways were downloaded from MSigDB50. Protein–protein network was downloaded from STRING database51, including protein interactions, co-expression and text-mined interactions.Drug sensitivity data, cell-line gene expression, somatic mutation and copy number variation data for 319 cancer cell lines and 153 drugs was downloaded from Genomics of Drug Sensitivity in Cancer (GDSC downloaded from 52 through GSEApy (https://gseapy.readthedocs.io/en/master/gseapy_example.html). We ran permutation test for 1000 times and normalized ES scores by the size of gene set to obtain normalized ES . We used the resulting pathway enrichment matrix with size of 319 cancer cell lines by 196 pathways as the gene expression feature (EXP).Gene expression data were measured by transcripts per million (TPM) and log-transformed. We imputed with mean for the rest of missing values. Enrichment score (ES) of each PID pathway in each cell line was calculated using the single-sample Gene Set Enrichment (ssGSEA) algorithm53, which calculates an enrichment score by measuring the closeness of pathway genes to a given gene set within a protein–protein interaction (PPI) network. We implemented the algorithm with some modification. The method is summarized as follows: First, both mutation gene and pathway genes were mapped to STRING PPI network51. For each cell, its mutation gene set is then used as the restart nodes to diffuse information through edges to their neighbors within the PPI by the Random Walk with Restart approach. For each pathway, a similarity score between the mutation gene set to the pathway genes is calculated by averaging all values on the pathway genes, where the node value represents the probability of being revisited . We modified the similarity score by multiplying each gene score with its gene expression value within the cell line, forming a cell-specific gene co-expression-mutation network. Then, a permutation test was performed 1000 times by randomly selecting the same number of genes, resulting in 1000 association scores as the background for the pathway. Pathway significance was normalized using z-score, resulting in pathway enrichment matrix with size of 319 cancer cell lines by 196 pathways for the mutation feature (MUT-Net).Mutation data are in long form, in which each row consists of a cancer cell line and its mutated gene name. We collected all mutations for each cell line to perform network-based pathway enrichment analysis by the NetPEA algorithmCopy number variation data were represented by the GISTIC (Genomic Identification of Significant Targets in Cancer) score comprising of − 2 (deletion), − 1 (loss), 0 (diploid), 1 (gain), and 2 (amplification), genes with GISTIC score of 0 were excluded. For each cell line, we collected a set of genes with copy number variations to calculate pathway enrichment score using the same procedure used for mutation data, resulting in the pathway enrichment matrix with size of 319 cancer cell lines by 196 pathways for the copy number variation feature (CNV-Net).54, a webserver collected curated drug-gene interaction data from literature. Second, we used the expanded gene list to find its neighbors within the STRING PPI network51, which increases the number of related gene per drug from 4.71 to 876.78 on average. For each drug, we performed pathway enrichment analysis against 196 PID pathways7 using its expanded target gene list. We used the original NetPEA approach described by Liu et al.53. i.e., we did not aggregate gene expression value to the node like we did for mutation and copy number variation data. As a result, we obtained the pathway enrichment scores in drug targets of size 144 drugs by 196 pathways for the drug gene network feature (DG-Net), excluding nine drug without target information or the target was missing from the PPI network.The primary target genes were provided in the GDSC data, we further expanded the target genes by two approaches. First, we obtained off-target genes from the DGIdb version 3 database55 with the open-source Python API, PubChemPy (https://pubchempy.readthedocs.io/en/latest/). We then converted SMILE strings into Morgan fingerprint with the open-source cheminformatics toolkit RDKit (http://www.rdkit.org), generating the matrix of 153 drugs by 256 molecular bits for the chemical structure feature (CHEM).We retrieved canonical SMILE strings by searching the PubChem database5 using Pytorch56, parameters used is listed in the Table S2. We performed tenfold cross validation for the experiments in this study with early stopping applied to avoid overfitting, repeated five times with different splits for the data for robustness verification and for computing the standard deviation between runs. Before feeding into the FNN model, we normalized our features using z-score. For the experiment of leave-one-drug-out, we took out one drug from training each time , and for the experiment of leave-one-cell-line-out, we took out one cell-line from training . We used RMSE to estimate generalization error of the model. For compared machine learning algorithms besides FNN, we used scikit-learn API57 for the other models, including the XGBoost algorithm with hyperparameter tuning and early stopping.We created a fully connected neural network (FNN), following an architecture suggested by Li et al.We tested the generalizability of PathDSP by training on GDSC and applying the trained model to the CCLE dataset. We then calculated MAE and RMSE for all samples and additionally for six drugs and 35 cancer cell line pairs shared between the CCLE and GDSC datasets.58 to estimate feature importance, which is measured by comparing the prediction value obtained with the feature and without it59. The python library SHapley Additive exPlanations (SHAP)60 was used to obtain global feature importance and visualization of feature importance at the local level. If a feature X has a positive shapely value, it indicates that feature X contributes to a higher predicted value, and vice-versa. Thus, in this study, it is interpreted as the feature X increases/decreases drug responses to drug Y (i.e. increase in −log(IC50), which means decrease in IC50).Feature importance was measured as the amount of contribution each feature makes to the prediction value. We used the Shapley valueSupplementary Information.


The outbreak of severe respiratory disease caused by SARS-CoV-2 has led to millions of infections and raised global health concerns. Lianhuaqingwen capsule (LHQW-C), a traditional Chinese medicine (TCM) formula widely used for respiratory diseases, shows therapeutic efficacy in the application of coronavirus disease 2019 (COVID-19). However, the active ingredients, drug targets, and the therapeutic mechanisms of LHQW-C in treating COVID-19 are poorly understood. In this study, an integrating network pharmacology approach including pharmacokinetic screening, target prediction (targets of the host and targets from the SARS-CoV-2), network analysis, GO enrichment analysis, KEGG pathway enrichment analysis, and virtual docking were conducted. Finally, 158 active ingredients in LHQW-C were screen out, and 49 targets were predicted. GO function analysis revealed that these targets were associated with inflammatory response, oxidative stress reaction, and other biological processes. KEGG enrichment analysis indicated that the targets of LHQW-C were highly enriched to several immune response-related and inflammation-related pathways, including the IL-17 signaling pathway, TNF signaling pathway, NF-kappa B signaling pathway, and Th17 cell differentiation. Moreover, four key components  showed a high binding affinity with SARS-CoV-2 3-chymotrypsin-like protease (3CL pro). The study indicates that some anti-inflammatory ingredients in LHQW-C probably modulate the inflammatory response in severely ill patients with COVID-19. Coronaviruses are enveloped RNA viruses, of which the pandemic causes a serious threat to humans and evolved into public health events Lianhuaqingwen capsule (LHQW-C), a Chinese patent medicine contains 13 medicinal herbs  is derived from two classics prescriptions: Ma Xing Shi Gan Tang (MXSGT) and Yin Qiao San (YQS). MXSGT, a notable classic formula recorded in Shanghan Lun edited by Zhang Zhongjing in Han Dynasty, is mainly prescribed for the treatments of febrile diseases, including influenza, acute bronchitis, and pneumonia A retrospective and observational study demonstrated that LHQW-C could improve patient clinical symptoms, including fever, cough, chest tightness, and decreased appetite LHQW-C was produced by Yiling Pharmaceutical Co. Ltd. , which composed of 13 herbs, including Fructus Forsythiae, Flos Lonicerae Japonicae, Herba Ephedrae, Almond, Radix Isatidis, Fortunes Boss fern Rhizome, Herba Houttuyniae, Herba Pogostemonis, Rheum palmatum, Rhodiola rosea, Glycyrrhiza uralensis Fisch, Mentha haplocalyx Briq and Gypsum Fibrosum. All ingredients of LHQW-C were retrieved from the natural product databases for Chinese herbal medicines: Traditional Chinese Medicine Systems Pharmacology (TCMSP) database Genecards was utilized to acquire COVID-19 related genes, which were the targets of the host A Venn diagram was drawn to visualize the intersected genes between the active ingredients and COVID-19 Proteins seldom act alone, they usually realize the biological functions through interactions with other proteins. Therefore, the String database was employed to predict PPI interaction data To determine biological processes and molecular interactions associated with selected common genes, Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) Network construction and analysis were performed using Cytoscape software (version 3.7.2) To validate the ingredient-target association and evaluate the effect of LHQW-C on virus suppression in our study, we used AutoDock Vina (version 1.1.2) for virtual docking 263 ingredients of the 13 herbs of LHQW-C were collected from the public databases  . The dupThe ingredient-target network comprised 153 (49 targets and 104 compounds) nodes and 299 edges . As shown in Figure GO enrichment analysis describes a complex cascade of events, which is accomplished by one or more components through well-organized molecular interactions KEGG pathway enrichment was performed by R version 3.6.3  and 9 targets  were selected for docking. In addition, we simultaneously chose 3CL pro for another docking to investigate whether LHQW-C has an effect on SARS-CoV-2. Before docking, the three-dimensional (3D) structures of the proteins 1alu, 5uui, 3w55, 1re1, 3il8, 4x51, 2nvh, 4g1w, 4kzn and 6lu7 were obtained by searching IL-6 , TNF, MAPK1, CASP3, CXCL8, IL-10, IL1B, MAPK8, VEGFA and 3CL pro from the PDB protein database, respectively. The docking scores were depicted in Table As shown in Figure in vivo are required to demonstrate the efficacy and safety of novel candidates, even some approved drugs, which means that it will consume more time for clinical practices. Up to now, there is no specific drugs for the treatment of COVID-19, the therapeutic strategies are still very limited Guideline for the Diagnosis and Treatment of Novel Coronavirus (2019-nCoV). In this study, network pharmacology was applied to explore the latent mechanism of LHQW-C in COVID-19 treatment.Since December 2019, a severe respiratory disease caused by a new coronavirus has swept the mainland of China and then discovered in over hundreds of countries and regions worldwide -1) on endothelial cells to promote adhesion of Neutrophils It was classically considered that virus surmounts the host defense and cause disease in some cases The Ingredient-target network showed that there are 158 ingredients in LHQW-C influencing the 49 genes that may play a role in COVID-19 treatment. As illustrated in PPI network, LHQW-C seemed to widely intervene in the production of inflammatory factors, including IL-6, TNF, IL-1β, and IL-2, especially IL-6, which was suggested to be used as clinical indicators in the prognosis and outcome of COVID-19 patients in vitro models also indicated that LHQW-C has anti-inflammatory properties Quercetin, a typical flavonoid, is one of the main compounds in LHQW-C has been shown to inhibit TNF-α, IL-6, IL-1β, and IL-2, thereby contributing to anti-inflammatory activity , KEGG enrichment analysis showed that TNF signaling pathway was highly enriched. Through the pathway, LHQW-C might protect alveolar endothelial/epithelial barriers by maintaining the integrity of intercellular junctions, eventually alleviating COVID-19 by improving pulmonary ventilation function.Postmortem results and pulmonary pathological collectively indicated that alveolar epithelial cell injury, the hyaline membrane formed in the alveolar cavity, monocyte, and macrophage infiltration. Also, there is severe type 2 alveolar epithelial cell hyperplasia, with some cells exfoliated 3CL pro is considered as a promising drug target, which plays a key role in the replication cycle of the virus Although we have described some interesting findings, there are still some potential limitations in our present study. Firstly, the databases for Chinese herbal medicines are incomplete, their accuracy and integrity cannot be guaranteed. Active compounds obtained from TCMSP with OB ≥30% and DL ≥0.18 may actually haven't good absorption. Additionally, several key compounds obtained by network pharmacology analysis may have false positive defects, but it can be improved using analytical technologies such as ultra-performance liquid chromatography (UPLC), liquid chromatography-tandem mass spectrometry (LC-MS/MS), and high-performance liquid chromatography (HPLC). Secondly, all of the data were based on in-silico analysis, there may be false positive and false negative interactions between compound-protein and protein-protein. Further experiments are needed to validate our findings.in vitro studies in vitro validation of the mechanisms of LHQW-C.In conclusion, we examined the potential working mechanism of LHQW-C for COVID-19 using an integrating network pharmacology method. Based on clinical Supplementary tables.Click here for additional data file.