Swarm three-satellite constellation for 4.7 years, to look for possible in-situ ionospheric precursors of large earthquakes to study the interactions between the lithosphere and the above atmosphere and ionosphere, in what is called the Lithosphere-Atmosphere-Ionosphere Coupling (LAIC). We define these anomalies statistically in the whole space-time interval of interest and use a Worldwide Statistical Correlation (WSC) analysis through a superposed epoch approach to study the possible relation with the earthquakes. We find some clear concentrations of electron density and magnetic anomalies from more than two months to some days before the earthquake occurrences. Such anomaly clustering is, in general, statistically significant with respect to homogeneous random simulations, supporting a LAIC during the preparation phase of earthquakes. By investigating different earthquake magnitude ranges, not only do we confirm the well-known Rikitake empirical law between ionospheric anomaly precursor time and earthquake magnitude, but we also give more reliability to the seismic source origin for many of the identified anomalies.The study of the preparation phase of large earthquakes is essential to understand the physical processes involved, and potentially useful also to develop a future reliable short-term warning system. Here we analyse electron density and magnetic field data measured by  The understanding of the underlying physical processes is likely to deliver the most reliable prediction method3. As it is practically impossible to follow this process at the level of the fault , an alternative is to study if the lithosphere interacts with the above atmosphere and ionosphere, i.e. assuming a Lithosphere-Atmosphere-Ionosphere Coupling (LAIC8), during this long-term phase, but with particular attention to the very last stages. Co-seismic coupling in the atmosphere is well established9, while the possible pre-earthquake coupling is more debated. A recent example is the possible ionospheric electron density enhancement before large earthquakes10.A large earthquake comes after a long-term preparation phase composed of different stages of seismicity evolution driven by the continuous but variable tectonic stress5 proposed a mechanism based on the theory of p-holes (positive holes), which are produced by the stress along the fault. When p-holes reach the Earth’s surface, they could ionize the atmosphere. These charged particles could create instability in the mesosphere and on the edge of the ionosphere. The mechanisms were tested successfully in laboratory11. An alternative mechanism is proposed by Pulinets and Ouzounov6, based on gas and fluid that could rise up toward the surface in the preparatory phase of the earthquake. Another model was provided by Kuo et al.12, which relies on a numerical simulation. It takes into consideration the role of the Earth’s magnetic field, suggesting a possible mechanism of alteration of the ionosphere that improves their previous model13.To explain the LAIC effects, different models have been proposed in the last years. Freund14.A third possible mechanism for the pre-earthquake electric field appearance is the possibility of modification of the electric field around the height of 100 km due to internal atmospheric gravity waveset al.7, Pulinets and Boyarchuk15 and Hayakawa16 presented a general review about the processes that can occur in the atmosphere and ionosphere before and during an intense earthquake, and their possible correlations.De Santis 23, Global Navigation Satellite System (GNSS) receivers25 and ULF magnetic field sensors28.To detect the pre-earthquake ionospheric anomalies, various parameters can be monitored by ground-based equipment such as ionosondes29 reviewed the most important results from the space investigations. The seminal satellite mission DEMETER30 was specifically designed to possibly identify a wide range of electromagnetic pre-earthquake signals31 and its statistical analyses were encouraging, pointing to LAIC above some reasonable level of randomness for 6.5 years of earthquakes34.ParrotSwarm satellite mission to the 2015 Nepal earthquake (M7.8) showed a correlation between the magnetic anomalies and earthquakes temporal pattern35. A similar approach, applied to other earthquakes provided promising results41.A promising application of the geomagnetic field monitoring by the in-situ electron density and magnetic field anomalies from Swarm satellites with earthquakes. For this scope, a Worldwide Statistical Correlation (WSC) analysis based on a superposed epoch approach has been applied to Swarm data for the first time. This approach is applied in a time window around earthquakes that occurred during a period of four years and eight months since 1 January 2014.Here we investigate the correlation of Swarm satellites to detect possible anomalies associated with the earthquakes from 1 January 2014 to 31 August 2018 (30 August for Ne). For the magnetic field anomalies we consider only the Y-East magnetic field component in the analysis (see Methods section for more details).We analyse the electron density (Ne) and magnetic field data from the three https://earthquake.usgs.gov), in terms of the time of occurrence, hypocenter location  and magnitude. We selected the same time span of the satellite data (from 1 January 2014 to 31 August 2018) and declustered the catalogue to remove dependent earthquakes42, in order to avoid bias in superposed epoch approach. For the purpose of this study, only earthquakes with the hypocentral depth less than 50 km are considered, since deeper earthquakes are less likely to affect the ionosphere43. The final dataset of 1312 M5.5+ worldwide shallow earthquakes was the base for the statistical correlation analysis with Swarm satellite data (see Methods section for more details).The worldwide earthquake data were extracted from the USGS  catalogue . Then we apply WSC to extract those anomalies (if any) associated in space and in time to the earthquakes and to obtain the superposed epoch diagrams (see Methods section for more details). Figures Electron density and Y magnetic component signals from all the t = 3.0 within 1000 km from epicentres (bins of 2.4 days × 3 degrees).1. Anomaly threshold kt = 3.0 within Dobrovolsky radius44 (bins of 2.4 days × 3.34 degrees).2. Anomaly threshold kFigures 3. Anomaly threshold kt = 2.5 within 1000 km from epicentres (bins of 2.4 days × 3 degrees).4. Anomaly threshold kt = 2.5 within Dobrovolsky radius (bins of 2.4 days × 3.34 degrees).t, we applied a larger value (kt = 3.0) for Ne than Y (kt = 2.5), because the former quantity is more variable than the latter, producing usually more outliers.Although our algorithm accepts any value of the threshold k10 (ΔT∙R), being ΔT the time between anomaly occurrence and earthquake origin time, while R is the distance between the location of the anomaly and the epicentre. The second one  is shown in panels (c) and associates the anomaly to the largest magnitude event among all earthquakes compatible with that anomaly.Please note that Figs. d parameter) and how many standard deviations σ the concentration is far from the simulated ones (n parameter) (for more details see the Methods). The statistical significance of the electron density anomalies is very low for the 1000 km analyses (d = 1.0–1.3 and n = 0.3–4.1), being comparable with typical random distributions of anomalies when Method 1 and 2 are applied, while is high for Dobrovolsky Area (DbA) analyses (d = 1.5–1.7 and n = 4.6–15.1). The 6–8 days anomaly concentration confirms previous results from DEMETER satellite34. The other longer precursory times had never been investigated so far and are the topic of the next section, where we will explain and justify this aspect in terms of the Rikitake empirical law45. It is encouraging that the methods that introduce some earthquake physical model and parameters such as Dobrovolsky strain radius as a limit of the spatial search  than Ne analyses .The Y magnetic field component analyses show larger values of The adoption of the Dobrovolsky strain radius slightly affects the main temporal features of the WSC analysis: although the anomaly density decreases . Generally the d values increase when the Dobrovolsky area and the maximum earthquake magnitude criteria are considered . The best results are reported when correlation is applied to the Y component of the magnetic field. Regarding the largest anomaly concentrations, some appear in almost all analyses, in particular 7, 12, 20 and 82 days before the earthquakes. Having enlarged the temporal window of analysis with respect to previous studies allowed us to detect also high precursor times such as 86 and 82 days before the earthquakes. They look significant and appear both in Ne and in Y.Table In general, one method to select the association of the anomaly with the earthquake emphasises anomalies closer to earthquake occurrences (Log(ΔT∙R), panel b), while another one tends to show also longer precursory times . Applying all methods, however, we find signatures in electron density and Y magnetic field component anticipating the earthquakes, from a few days to around 80 days and the statistical correlation obtained makes this precursory feature compelling.45: Log10(ΔT) = a + bM. To deeper investigate this concept, we extended our analysis to 500 days before the earthquake occurrences, applying Method 1, i.e. without imposing any earthquake-anomaly association, in order to not favour closer or farther anomalies with respect to earthquake occurrences. We considered both the electron density and the Y-component of the magnetic field within the Dobrovolsky area and grouped the correlations for the following individual bands of earthquake magnitude: 5.5–5.9, 6.0–6.4, 6.5–6.9, 7–7.4, 7.5+. Of course, the choice of 500 days could be critical because for a large number of events (i.e. the ones occurred in the first 500 days of 4.7 years of investigated period) not all the time domain was covered by our analysis. On the other hand, this would allow us to look at longer potential precursor times.From what we carried out and summarized in Table 45, we included this largest range of magnitude for the electron density. We fit the same Rikitake functional law to our precursor times with respect to the central value of magnitude for each band. We estimated the error bars for the magnitude as the half width of the investigated range and for the time interval as the bin span of the anomalies concentration. Figure 45 (thick lower and upper lines). It is surprising that, within the estimated errors, we find similar a,b values  to those proposed for ground magnetic observations by Rikitake45 (see Methods section).Figures 20.Apparently the two fitting lines for Ne and Y are distinct, but both fits are within the errors. In addition, for both quantities, it appears that earthquake precursors occur within a day for events with magnitude below 4.2, although is it very unlikely that these weaker earthquakes could produce an effect in the ionosphereOn the other hand, the greater the earthquake magnitude, the greater the difference between the ΔT referred to Ne and Y-component: for instance, for M7.5, the Ne relation provides ΔT = 363 days, while Y relation gives ΔT around 1000 days. This could explain why we do not find a statistical significance in the M7.5+ analysis for Y magnetic component.45 for ground magnetic observations, even extended it to electron density and magnetic field satellite data, with a little adjustment of the coefficient values.This result strongly supports the empirical law found by Rikitake46 across the Dobrovolsky strain radius RDb44, we obtain the relationships a = −Log. We can verify them by comparing the b value obtained from Rikitake (around 0.8) that, within the error, resembles the value of 0.86 deduced from Eq. a, we can even estimate D. Although a value has large uncertainty, i.e. a ≅ −2 (Rikitake), a ≅ −3.3 and a ≅ −3.9 , we can take the central value of a ≅ −3, obtaining D ≅ 100 m2/s, which is one-two order more than a reasonable value for the crust1. However, it is really interesting that this same order of diffusivity can be found for slow earthquakes when a diffusion model is considered47. This interesting coincidence would merit more future attention, potentially opening new perspectives in seismological studies.The Rikitake law is reasonable for the process of earthquake generation and coupling with the above atmosphere and ionosphere layers with simple arguments (more details are shown in Methods). Assuming a lithospheric process of stress diffusionSwarm satellite observations highlight that anomalies appear to occur before the earthquake occurrences, between a few days and 80 days before the earthquakes, with larger peaks at around 10, 20 and 80 days. We find that in all analysed cases considering the DbA, the largest concentration of pre-earthquake anomalies is statistically significant by a factor up to around 2 (i.e. d ∼ 2) times the simulated data, with up to 15–17 σ (i.e. n = 15–17). We note that the detected anomalies seem better correlated with earthquakes of stronger magnitude.The electron density and magnetic field WSC analyses applied to about 4.7 years of a, b coefficients are, within the estimated errors, close to those proposed by Rikitake45. We also provide a simple explanation of this relationship and show that the empirical expressions we found for satellite data anomalies are consistent with a stress diffusion process in the crust as that producing slow earthquakes.We confirm linear relation between the LogΔT and magnitude, and the found The Rikitake law, confirmed by the separate analyses of Fig. Although this investigation would support the LAIC with clear statistical significance, another clear message emerges: not all earthquakes are in the favourable conditions to produce significant effects in the ionosphere. Only a portion of them  generates a non-negligible electromagnetic effect that cannot be due to simple chance. Several causes can be attributed to this deficiency: insufficient satellite passes available, an inappropriate coversphere (e.g. vegetation), adverse meteorological conditions, and/or still not perfect anomaly detection strategy.Swarm satellite mission is greatly encouraged: for example, with at least 10 years of data, longer precursor times could be investigated without loss of statistical significance. Data from other quasi-polar satellites, equipped with magnetometers and Langmuir probes (e.g. the Chinese seismo-electromagnetic satellite48), would also be useful for this scope.More detailed analyses, as the study of the type of earthquakes analysed, according to their occurrence in subduction zones or along strike-slip faults, in the land or under the sea, in interplate or intraplate, and the inspection of single anomalies, would help to better understand the physics behind the possible coupling phenomena. In this respect, a prolongation of the 8, by which we can consider the Earth system as an ensemble of cross-interacting parts. Therefore, it is mandatory to consider a multiparametric point of view to address this kind of complex phenomena, by combining seismological, atmospheric and ionospheric information40.A final point is clearly important here: since the ionospheric anomalies are causally related to what happens inside the Earth and at the ground-to-air interface, the corresponding parameters should be, or even must be, included in any further study. This is implicit in the Geosystemics conceptSwarm mission is an ESA satellite mission49 composed of three identical quasi-polar orbiting satellites, two  at lower orbit (around 460 km above the Earth’s surface) and the third (Bravo or Swarm-B) at the highest orbit (around 510 km). The satellites were placed in orbit on 22 November 2013 and are still orbiting around the Earth. The original configuration with Swarm-A and Swarm-B flying in parallel and Swarm-C in a higher orbit49 was changed to the present one because of early problems (from 5 November 2014) in the scalar magnetometer of Swarm-C.The satellite payloads comprise, among others, a Langmuir probe (LP) and two magnetometers, i.e. a vector fluxgate magnetometer (VFM) and an absolute scalar magnetometer (ASM). These sensors have been here analysed systematically to detect possible pre-earthquake Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) electron density and magnetic field anomalies from space.Swarm satellites with a sampling rate of 2 Hz. The input data have been provided by the original Swarm Advanced product called “2_Hz_Langmuir_Probe_Extended_Dataset”. This dataset is provided in Common Data Format (CDF) and freely available in the ESA Swarm FTP and HTTP Server swarm-diss.eo.esa.int . The current release of 2 Hz Langmuir Probe Extended Dataset (Release 101) is the ESA reprocessing of all Swarm Electric Field Instrument data from the beginning of the mission to 30 August 2018.The Ne data used in this work are measured by the LP of https://earth.esa.int/web/guest/missions/esa-eo-missions/swarm/data-handbook/preliminary-level-1b-plasma-dataset). This is not an issue for the purposes of our anomaly detection, as it is not based on the absolute value itself, but its derivative as reported later in the text.According to ESA, the current values of electron density are up to a few 10% too high at low density  and vector X,Y,Z magnetic components. The data are at 1 Hz sampling that correspond to the low resolution magnetic data contained in the Level 1b (L1b) products. All the L1b magnetic data are provided by ESA in Common Data Format (CDF) and hosted in the ESA https://earth.esa.int/web/guest/swarm/data-access/quality-of-swarm-l1b-l2cat2-products), we took into account the quality flags associated with both the electron density and magnetic field data.In order to select data without evident troubles/problems during the satellite flying  reference frame by the VFM instrument and the magnetic absolute intensity by ASM instrument .Swarm satellites can be estimated to be less than 0.3 nT, with a typical value of 0.1 nT50.The error of the magnetic field measured by NeAD  and MASS , applied to the electron density Ne (2 Hz sampling) and magnetic field  from Swarm satellites , respectively. These algorithms share the main features that we highlight below.The approach to identify anomalies is based on two novel algorithms, i.e. 51). Then, the first time derivatives of Ne and of the magnetic field are estimated as the first difference values divided by the time interval between two consecutive samples. Finally, a fit with cubic splines is applied to remove the long term trend.For each day and for all the satellites tracks (the daily number of semi-orbit for each satellite is about 32), the Local Time (LT) and the geomagnetic latitudes are evaluated, the latter based on a tilted geocentric dipole field from the last generation of the IGRF  global model of the geomagnetic field  and February 14, 2016 . The track number, descending (D)/ascending (U), the corresponding local time (LT) and UTC, the geospace conditions given by the Dst and ap geomagnetic indices, areprovided. The geographical area of interest with satellite track (red), the Dobrovolsky area  and the earthquake epicentre (green star) are also shown. It is worth noticing that, from Figure 39. This could be simply explained by field aligned current processes that do not practically affect the total intensity of the magnetic field vector, but only its direction35. Accordingly, instead of analysing the whole magnetic field, we focused only on the magnetic Y component because it is less affected by external perturbations than X and Z components52 and this increases the possibility to detect Earth internal source anomalies.Figures NeAD and MASS outputs (for the entire Swarm data set) are further analysed by overlapping sliding windows within ±50° geomagnetic latitude, to limit the effects due to the high latitude ionosphere, and under quiet magnetic conditions in terms of Dst and ap indices  to limit the effects of perturbations coming from the outer space.To detect anomalies of interest for the WSC analysis, 53. The approach with overlapping sliding windows provides an output matrix in which each row identifies a given window and each column contains the following quantities: the date and central time (UTC and LT) of the given window, the satellite , the track number, Dst, ap, the root mean square error (rms) over the samples distribution within the given window and the frequency content of the window (the latter not used in this work), together with the root mean square error (RMS) over the whole track in ±50° geomagnetic latitude.We consider overlapping sliding windows of 7.0° latitude length, moving by 1.4° (1/5 of window length) along the whole ±50° geomagnetic latitude range. Since the satellite speed is of about 7.6 km/s, the choice of the 7.0° sliding window allows us to include typical pre-earthquake satellite signals of some tens of seconds into a sufficiently short spatial lengtht∙RMS, where kt is an appropriate threshold  and RMS is the root mean square error computed for the whole track.Finally, from the output matrix, we define as anomalous those windows  for which rms > kNeAD and MASS to detect anomalies are:Swarm tracks within ±50° geomagnetic latitude;p ≤ 10 nT during the track acquisition time;Low magnetic activity: |Dst| ≤ 20 nT, asliding windows of 7.0° latitude length, moving by 1.4° along the tracks;t∙RMS .rms (of each sliding window) > kSummarizing, criteria adopted by NeAD and MASS with the earthquake locations and occurrences. The earthquake catalogue was declustered first by extracting all M5+ earthquakes, then detecting and removing the dependent earthquakes, i.e. those earthquakes with magnitude M ≤ Mms−1 (Mms is the mainshock magnitude) that happen inside 10 km from the mainshock epicenter and a 10-day time window from its origin time. In the declustered catalogue, the magnitude of the mainshock is replaced by the equivalent magnitude of the seismic cluster. We then selected those shallow earthquakes with M ≥ 5.5 for which LAIC signatures are more likely to be captured as highlighted by Liu et al.20 who found a dramatic enhancement in the statistical correlation between ionospheric anomalies and earthquakes with magnitude greater than 5.4. In addition, this choice avoids problems of catalogue incompleteness54.The WSC algorithm evaluates the possible correlation in space and time of the detected anomalies by Swarm mission data (4.7 years). The horizontal axis of the WSC diagrams is the time lag between the anomalies and the seismic events and the vertical axis is the distance of the anomalies with respect to the epicentre (in degrees). The common time origin is represented by the white vertical line. The colour bar identifies the density level of the anomalies . A simplified flowchart of the WSC algorithm is shown in Figure 34, apart from the use of a wider time window around the earthquakes . We considered a longer time window according to the empirical relationship given by Rikitake45: for a typical M5.5 earthquake, which is the most frequent in our dataset, we would expect a precursor at a mean advance time of 16 days, in a range from 2 to 144 days . Therefore, extending the analysis to 90 days before the earthquake occurrence, seems to be a good compromise, avoiding border effects in time. In space, we consider either an area comprised by a fixed radius of 1000 km around the earthquake epicentre34 or the most popular Dobrovolsky’s circular area (DbA) around the earthquake epicentre, whose radius RDb in km scales with magnitude M, i.e. RDb = 100.43M, as suggested by Dobrovolsky et al.44. For our selected earthquakes, the radius of this area is between 230 km or 2.2° (M5.5) and 3700 km or 33.4° (M8.3). The DbA is considered a good empirical approximation of the preparation area of an impending earthquake56.WSC cumulates all the anomalies of the same family (Ne or Y magnetic field component) associated with each earthquake  in a unique space-time graph by a superposed epoch approach having as common origin the occurrence time and the epicentre of all investigated earthquakes along the available In addition to the adopted spatial  and temporal  criteria, we present the analysis in three different ways.The first method (Method 1) does not take into account any assumption, associating each anomaly to all earthquakes that fall inside the analysed space time. In the next two methods, we introduce some limitations to prevent an anomaly from being associated to more than one earthquake, which is very unlikely to happen. This can be done by:10 (ΔT∙R) is minimum, where ΔT is the time lag between the anomaly occurrence and the earthquake , and R is the spatial distance of the anomaly from the earthquake epicentre. This method intends to assign to the anomaly the closest earthquake in space and time. This limitation takes also into account the correlation found between Log10 (ΔT∙R) and the earthquake magnitude M when ionosphere anomalies from HF ionosondes are considered23.Method 2: Referring a given anomaly to that earthquake for which LogMethod 3: Referring a given anomaly to that earthquake with the greatest magnitude M , falling inside the analysed space-time window.The Method 1 has the advantage to not impose any further constraints on the anomalies, but has the disadvantage to have eventually some anomalies with more than one associated earthquake.We would underline that the selection of a particular method affects only a small percentage of the whole cases. So, all the methods have a common anomaly-earthquake association.Therefore, we provided space-time distributions, one for each earthquake, of the anomalies (Ne and/or Y-magnetic field component) associated with it. Then we superposed all the distributions imposing a common origin that identifies the earthquake occurrence times and the epicentres. The resulting WSC distribution  contains all the anomalies overlapped. The density level of the anomalies  is estimated in 50 temporal bins and 10 spatial bins, so each bin of the diagram usually covers 2.4 days (120 days divided by 50 bins). The epicentral distance  of each bin is of 3°, i.e. about 330 km at the Earth’s surface for the 1000 km analyses , up to 30° . The small difference in the two full-scales is due to the different height of the bins in the two kinds of analysis. In the 1000 km analyses, it allows to complete the whole distance with three full bands, but almost preserving the possibility to compare these analyses with those made across the DbA. In any case, to maintain perfect agreement in all (1000 km and DbA) analyses the anomalies and earthquakes have been counted always in a bin of 3.34°.The statistical significance of the WSC analysis results is based on the introduction of quality statistical quantities as follows.MAX and that of a theoretical uniform distribution (in space and time) D0 of anomalies, i.e. DMAX/D0.To estimate how much reliable is the largest concentration of anomalies, we firstly considered the ratio between the largest concentration of the real anomalies D0 can be analytically defined as:t is the analysed time in days, in this study it is 1703 days, i.e. 4.7 years. Neq is the number of earthquakes associated with at least one anomaly. Thus, the units of D0 are (square degrees × days)−1.DMAX is calculated as:DNexan is the number of anomalies associated with earthquakes in the bin of the first row  with the largest concentration of anomalies; ABIN is the area of the bin (i.e. the area of the circle or of the annulus region of the first bin around the epicentre); ΔtBIN is the time width of the bin in the same unit of Δt (2.4 days if we investigate a window from 90 days before to 30 days after the earthquake occurrence).MAX/D0 is biased because the areas associated with the earthquakes could overlap, either in case of considering circles of 1000 km around the epicentres or the effective DbA. In addition, we do not analyse all the temporal periods but only those with low magnetic disturbance , to roughly “filter out” those anomalies clearly depending on solar geomagnetic activity.Note, however, that DMAX/D0, we compare it with its analogous obtained by correlating the real earthquake dataset with a certain number  of random distributions of anomalies , almost homogeneous in space and time. The random anomalies are generated assigning to each of them a latitude, a longitude and an occurrence time. The latitude and longitude are selected (with homogeneous probability in space) within the analysed global area (i.e. the area with |geom. latitude| ≤50°). The occurrence time is selected among the geomagnetic quiet times with uniform probability. From the random simulations, we also calculate the average and the standard deviation σrand of the parameter [DMAX/D0]rand, to be further compared with that one corresponding to [DMAX/D0]real for the real cases.Then, to establish the actual statistical significance of Drand.Table d defined as the ratio between [DMAX/D0]real /σ rand. Also in this case, the larger n, the more significant the corresponding real analysis.Finally, to increase the WSC reliability, we provide also the parameter We can provide a rough estimate of the fraction of earthquakes that produce ionospheric effect in two ways. A way is based on the best cases of Table 45, that relates precursor time ΔT with the earthquake magnitude M:a = −2.08 (±1.43) and b = 0.78 (±0.23), for geomagnetic field ground observations45.We recall the empirical law by Rikitake46 across the DbA, we can relate the spatial distance R (in km) of the anomaly from the earthquake epicentre with the time ΔT:Although out of our scope, we sketch some simple reasoning on why the Rikitake law is reasonable for the process of earthquake generation and coupling with the above atmosphere and ionosphere layers. Adopting a lithospheric process of stress diffusionet al.44 we can express RDb, as:M the earthquake magnitude.According to Dobrovolsky RDb, we can replace RDb in , lead investigator of the Supplementary information.


The WHO Africa Region faces a shortage of health workers due to inadequate production of health workers and emigration of physicians and nurses to wealthier countries. South Africa and the United States share a history of discriminatory policies and practices resulting in groups that are under-represented amongst health professionals. One US response is the Area Health Education Centers Program (AHEC), a pipeline program to recruit members of under-represented groups into the health professions.(1) Compare and contrast the United States’ AHEC model with that developed in South Africa by Stellenbosch University Faculty of Medicine and Health Sciences SA AHEC in partnership with Morehouse School of Medicine in the United States. (2) Describe a formative evaluation of the Stellenbosch AHEC Program.Four hundred students (grades 7–12) and 150 teachers participated in SA AHEC with the goal of preparing the students to better compete for university admission. Students received after-school tutoring, holiday schools, and counselling on study skills, health careers, and university entry. Educators received continuing professional education, classroom observation, and feedback. The program was evaluated through a series of interviews and focus groups involving AHEC staff, educators, and parents and caregivers.Program strengths included educator training, collaboration, and increased student maturity, motivation, and academic success. Challenges included limited time with students, the location of some sites, and the educators’ need for more engagement with AHEC staff and schools. Quarterly workshops were conducted to address challenges. Over 50% of program alumni are currently enrolled in institutions of higher education. Students will be tracked to determine whether they are able to complete their health professions studies and return to the communities where they grew up, or to similar communities.With appropriate adaptation and attention to context, it might be possible to implement similar programmes in other African countries. The comparison of the United States and South African models suggested that more parent and teacher participation in an advisory capacity might help to avoid some challenges. The WHO Africa Region faces a severe and chronic shortage of health workers, with over 800,000 physicians and nurses alone required to meet estimated minimal needs . This shThe situation is less dire in South Africa than in most other sub-Saharan African countries, with 0.776 physicians per 1000 population and 5.114 nurses per 1000. This compares favourably with (for instance) 0.40 per 1000 and 3.35 per 1000, respectively, in neighbouring Botswana and (as an example of a less-developed country) 0.107 per 1000 and 0.529 per 1000, respectively, in the Democratic Republic of Congo. However, South Africa lags far behind developed countries such as the United States, which has 2.3 physicians per 1000 population  getting almost 100% attendance at parent meetings.”“Teachers are coming to professional learning sessions over weekends.”Education Stakeholders. For tutors who had been a part of the program for more than one year, seeing their students progressing and being successful were regularly cited. Some of the tutors who were new to the program stated that having fewer “bad” behaviour issues and having students willing and ready to learn was a good accomplishment. Hearing positive feedback from those in the community was also mentioned as one of the greatest progress areas to date. Selected responses related to these results are included below:“They [students] e-mail me and said no, the final exam was good. And I’m really proud of myself about that. I must be proud of myself because I did something right.”“[I] see learners achieve and doing great. And kind of let me say people are talking about it – yeah … weekends I normally go fishing, and I meet people. And so we started talking and they would say – talk about the program, and I say, okay, yes, I know the program.”Parent/Caregivers Stakeholders. Parents’ reported satisfaction with the AHEC program was high. Parents expressed that the most important parts of the AHEC program were the motivation that the program gave their child to succeed and the opportunity the program provided for their children. By participating in the AHEC program, they noted, their children were not limited to their parents’ financial resources or the community in which they lived. Parents observed that their children were more focused on their school work and that their grades improved as a result of participating in this program. Selected responses related to this result are included below.“For my son, it was a huge motivation ’cause his marks increased last year in math from about 14 to 17. He’s more committed to it. He loves to study.”“And at the end of the day, it’s an opportunity for our children, my daughter in particular, to achieve that goal that she had since age six. Like she says, she’s always told us at a young age she wants to become a doctor. And now she’s been given the platform to start reaching out to [achieve that goal].”“It’s given her lots of self-confidence, and it’s given her motivation.”SUNCEP Stakeholders. Program timing and logistics were frequently cited challenges. The program initially started late in the school year, which meant administrative and teaching staff were charged with developing their roles and responsibilities within a truncated time period. Logistically, participants in and from rural areas had to travel great distances in order to participate. This created a hardship for a number of learners, their parents, and their tutors. Representative responses are included below:“Increasing the contact time at holiday schools. At the holiday schools, we see students for five consecutive days, and then won’t see them again for another two months [until the next holiday]. This is too long a time for students to go with no contact.”“Locations are more dispersed [rural areas – up to 400 kilometres/250 miles between schools], so getting the parents together is much more difficult than in grade seven intervention, where all of the schools are located in similar areas.”Education Stakeholders. Tutors and curriculum advisors discussed challenges related to their new roles with AHEC. Tutors indicated that lack of familiarity with the students in the program includes current school performance in addition to the general rapport they have through more extended daily interaction as noteworthy. The biggest challenge cited by curriculum advisors was the travel cost for students who lived far from the site of the holiday school. Additionally, advisors noted that communication from students’ regular schools was poor, making it difficult to arrange food and lodging. Selected responses related to these results are included below:“I need the schools to really realize that say [they should tell me] the fact around the numbers – the total of learners and that they should step up their feedback to me. That is one of the greatest challenges, I think.”“I don’t know what their marks [are at] school. I thought that that might be something that we could work from, but [it is not] … But it would be good to see them.”Parents/Caregiver Stakeholders. When parents were asked to discuss things that make participating in the AHEC program difficult, conflicts with other activities were cited. Parents generally placed a high priority on the AHEC program and were forced to find alternatives to the activities with which it conflicted. Selected responses related to this result are included below.“So, there are clashes with other activities at the school, but we as parents wanted to – this is more important to take part in this program than anything else that’s happening on Tuesdays or Thursdays at the school. Like, for instance, she had a leadership meeting, which she had to miss because of this program, which did agitate her somewhat. But we’ve dealt with that and said that this program is more important. That meeting will have to take place some other time, or we’ll have to catch up some other way.”SUNCEP and education stakeholders also discussed the relevance of South African history, politics, education, economics, and cultural realities and challenges. The historical implications of class, race, and economic realities of the present were positioned as related to challenges in the educational system and the need for AHEC. Most learners were from low-income or working-class families, with parents who sacrificed their wants for their children’s best interests. Other issues that were positioned as both community and school problems were gangs, violence, and drugs. Most directly related to AHEC were questions related to how certain groups were apparently selected to participate in the programs and others were not. Selected responses from SUNCEP and education stakeholders related to these results are included below:“Crisis in education in South Africa.” (SUNCEP Stakeholder)“Big divide between the well-performing schools and the schools that are not doing that well.” (SUNCEP Stakeholder)“Teacher professional training [is important] because a lot of teachers that are coming from those schools are sometime de-motivated.” (SUNCEP Stakeholder)“I think through all the processes that SUNCEP is involved, part of what we’re doing is to make access to the University of Stellenbosch possible for people who were excluded in the past.” “Communities are not affluent – low SES.” .“For instance, if you look at the communities nowadays, we have problems with violence, gangsterism, and especially what is really killing society, especially in Cape Town and in [Malmesbury], is what they call meth. It’s really killing people, and what children are doing to get money for the drug – wow. So, on the one end, it’s great opportunities for learners to be part of this program and keep them away from, say, the violence and stuff, and keep them out to realize there is a better life after all. Because if you take a look at our school now, it’s a lot of learners – it’s as if they have lost hope, so they just go [drop out of school].” Most student results were associated with their general impressions of the program. Learners discussed and detailed perceived benefits of the program, which included improved marks, the ability to achieve goals, investment in their future success, and the opportunity to receive a bursary for university. Perceived benefits included the tutors’ styles of teaching, different methods that are taught for solving problems, the ability to build relationships with learners and tutors, the challenge the program provides, and the sheer amount of learning that takes place during the program. Recommendations towards proposed improvements in the program included wanting see more children having the opportunity to experience the program, based on their own satisfaction as participants. They also described the transport they currently have (or lacked) as being a challenge. Learners also cited the need to add more experiments and use of technology or visuals to prepare them for university.Of 165 AHEC Programme alumni, 83 (50.3%) were known to be enrolled in institutions of higher education in 2017 . Of the 85, 11 were pursuing an MB, ChB degree, and 8 were in other health profession bachelor degree programmes. Other BSc degrees were sought by 17 students, and engineering degrees were pursued by 16 students. The other 32 were enrolled in a wide variety of programmes.Eleven alumni were classified as prospective students at Stellenbosch. The remaining 71 were lost to follow-up; they were not enrolled at Stellenbosch but might be enrolled in another institution of higher education.Key stakeholders all noted multiple program successes. Teachers and tutors seemed to benefit from training and collaboration. Parents, tutors, and SUNCEP staff saw increased maturity, academic advancement, and increased motivation amongst students.However, there were challenges as well. Program timing (i.e. starting late in the academic/school year) was described as difficult for stakeholders, district officials, and tutors. Limited academic time and the location of some holiday schools created teaching and other logistical challenges for parents, as well as for tutors and district officials. Continuity of curriculum development and skill measurement were areas of concern expressed across interviewees. Educators believed their efforts with learners would be impacted by the new Curriculum Assessment Policy Statements (CAPS) curriculum and related requirements associated with standardized testing. Subsequently, major strides were taken so that all tutors teach using CAPS.Communication was also a prominent theme throughout the educator interviews. Tutors longed for more communication directly from AHEC leadership, and advisors desired better communication from the schools that they served. Tutors specifically mentioned not being informed of their students’ progress as a challenge. Tutors also complained that they received no communication from the schools about the students participating in the program; hence, tutors have no information about the students’ abilities and must spend time establishing a baseline before they can begin teaching. Moreover, soon after this evaluation, several workshops were held on a quarterly basis to ensure communication among all stakeholders in the program.Stakeholders made recommendations that included increased program awareness; steps to address social, economic, and political challenges; program restructuring; increased communication amongst stakeholders; and opportunities for additional time with students. Cross-cutting recommendations cited by at least two stakeholders groups are included in Table With at least half of the alumni having achieved university admission, the program must be deemed an early educational success. From the perspective of a medical educator, it might be disappointing that more students are pursuing engineering than medicine, but South Africa also has a shortage of engineers . Of courThe United States and South Africa have much in common: a shortage of physicians and other health professionals, particularly in rural and impoverished areas; population groups who have suffered from discrimination and bear a disproportionate burden of disease; and an under-representation of these same groups in the medical and health professions workforce, even though physicians who are members of these groups are more likely to serve those groups.There are important differences as well: health indices in the United States are much better than those in South Africa; shortages of personnel are less severe; and South Africa suffers from the emigration of many of its physicians, whereas the United States is one of the beneficiaries of this “brain drain”.Although AHEC is intended to address the problems that the two countries have in common, the models upon which the programs are built are somewhat different. The major difference is in the nature of the remote site. US AHEC centres, whether attached to regional hospitals or established as separate corporations, are independent of the affiliated medical school . The rurThis is not to say that one model is superior to the other. It would be inappropriate to impose the US model on the South African team, although the goal of building a pipeline is the same in both countries. It might be argued, though, that whereas the SU-AHEC program was an educational success, some of the logistical problems could have been prevented had there been a local voice or voices on the advisory board to point them out before they occurred.Although the views of AHEC program participants are documented here, limitations of this study are acknowledged. Due to constraints of convenience, proximity, and time, not all AHEC sites were engaged for participation in key informant interviews and focus groups, limiting the degree to which results are representative of those AHEC sites not visited by the evaluators and do not necessarily reflect the views of all stakeholders. Additionally, in the parent groups there was a language barrier, which required the use of an interpreter. The interpreter sometimes added clarifications or additional comments, but this was not consistent across groups.The SU-AHEC demonstrated its strength in building the proximal end of the health professions pipeline. The parents (mostly coloured) who participated in the program demonstrated great motivation to prepare their children for university entry. There were logistical obstacles in the low-income rural areas where the program took place, but participants did their best to overcome them. The children will be tracked over the next few years to determine whether they are able to complete their health professions studies and return to the communities where they grew up, or to similar communities, in order to help address disparities in health, health services, and health professions. South Africa is classified as a middle-income country and thus has more resources to devote to programmes of this type, and it has fewer impediments compared to less developed countries. However, if the program is successful in South Africa, it may be possible to adapt the model appropriately and create similar pipeline programs in other sub-Saharan African countries. There are factors that distinguish the South African AHEC model from that of the United States. The strengths and limitations of the two models must be taken into consideration in developing programs in other countries.


Differential scanning calorimetry and anisotropy results indicated formation of α-lactalbumin complexes with doxorubicin or paclitaxel. Furthermore, molecular docking and dynamic studies revealed that TRPs are not involved in α-Lac’s interaction with Doxorubicin while TRP 60 interacts with paclitaxel. Based on Pace analysis to determine protein thermal stability, doxorubicin and paclitaxel induced higher and lower thermal stability in α-lactalbumin, respectively. Besides, fluorescence lifetime measurements reflected that the interaction between α-lactalbumin with doxorubicin or paclitaxel was of static nature. Therefore, the authors hypothesized that α-lactalbumin could serve as a carrier for doxorubicin and paclitaxel by reducing cytotoxicity and apoptosis which was demonstrated during our in vitro cell studies.Doxorubicin and paclitaxel, two hydrophobic chemotherapeutic agents, are used in cancer therapies. Presence of hydrophobic patches and a flexible fold could probably make α-Lactalbumin a suitable carrier for hydrophobic drugs. In the present study, a variety of thermodynamic, spectroscopic, computational, and cellular techniques were applied to assess α-lactalbumin potential as a carrier for doxorubicin and paclitaxel. According to isothermal titration calorimetry data, the interaction between α-lactalbumin and doxorubicin or paclitaxel is spontaneous and the K (M Doxorubicin (DOX) and paclitaxel (PTX) are among the most effective chemotherapeutics used in the treatment of various types of cancer. DOX, an antibiotic from the anthracycline family, exhibits a broad spectrum of antineoplastic activity. In fact, DOX is one of the most powerful chemotherapeutic agents in breast cancer treatment and is used for treatment of other malignancies as well. The therapeutic activity of DOX is mainly due to its intercalation and alkylation of DNA and induction of topoisomerase II mediated strand breaks5. Another potent anticancer agent, PTX, is used for the treatment of a wide spectrum of cancers, especially ovarian and breast cancers. PTX is extracted from the bark of Taxus brevifolia and is a diterpenoid taxane derivative. Its unique mechanism of action is via binding tubulin dimers and stabilizing microtubule formation, which ultimately disrupts mitosis and results in cell death8. However, DOX and PTX are very hydrophobic and thus poorly soluble in aqueous media, which presents critical challenges for clinical application9. Therefore, several approaches have been investigated to develop novel aqueous formulations including, the use of liposomes, micelles, nanoparticles, nanocapsules, polymeric microspheres, and bioconjugation with different molecules, e.g., albumin, etc8. As an indication, Doxil®, the first FDA-approved nano-therapeutic agent, is a liposomal preparation of DOX effective in the treatment of ovarian and some other forms of cancers10. In the case of PTX, Abraxane®, an albumin bound nanoparticle, is a nanovector with special application in the treatment of breast cancer and non-small cell lung cancer12.In spite of intense research, cancer which claimed the lives of 8.2 million people in 2012, is still a major global threat13. Moreover, they have a broad range of characteristics such as biodegradability, exceptional binding capacity for different drugs, as well as less opsonization by the reticuloendothelial system (RES)14. For instance, human serum albumin, bovine serum albumin, and the milk protein β-lactoglobulin have been studied as carriers for DOX16. In particular, milk proteins have numerous structural and functional properties which could facilitate their application as delivery agents of a variety of bioactive compounds. They can bind to ions and/or hydrophobic molecules with various degrees of affinities, and are widely available, natural, inexpensive, biocompatible, and biodegradable17.Various proteins have potential as nanocarriers in drug delivery, and are being considered as GRAS 19. α-Lac is involved in the biosynthesis of lactose as a component of lactose synthetase20. Hydrophilic α-Lac is present in the milk of nearly all mammals and composes 3.4% of total bovine milk proteins (1–1.5 g/l)21. This calcium binding metalloprotein contains two domains; a large helical domain (residues 1–34 and 86–123) and a small β sheet domain (residues 35–85) connected by a loop. The α helical domain made up of three main α helices  and two smaller 310 helices (residues 18–20 and 115–118). The β sheet domain consists of three antiparallel β strands , a 310 helix (residues 77-80), and several loops. There is a deep cleft between the two domains and a calcium ion is connected to its binding site in the loop connecting the two domains (in the native α-Lac). Ca2+-bound and -depleted forms of α-Lac are referred to as holo-α-Lac and apo-α-Lac, respectively. It is important to note that, apo-α-Lac presents altered tertiary structure compared to holo-α-Lac22. α-Lac also contains eight cysteine (Cys) residues, which form four disulfide bridges  and one hydrophobic pocket23. Interestingly, it has been shown that oleic and palmitic acids can only bind to apo-α-Lac which harbors one binding site for oleic acid and another for palmitic acid24. The complex formed by apo form of human or bovine α-Lac and oleic acid demonstrate cytotoxicity in various tumor cell lines25. These complexes exist in a core-shell configuration where fatty acids or other hydrophobic substances are encapsulated by several partially denatured, flexible protein chains27. α-Lac has also been shown to complex with other hydrophobic compounds, including fatty acids29, retinol17, hydrophobic column chromatography phases, hydrophobic peptides, melittin of bee venom, and vitamin D332.α-Lactalbumin (α-Lac), the second most abundant among milk whey proteins, is an acidic (pI 4.8), low molecular weight (14.2 kD) globular protein33. According to the results, α-Lac and the prepared nanosystems presented less toxicity in normal compared to tumor cells. This already has been determined in the case of human α-Lac-oleic acid complex (HAMLET) and bovine α-Lac-oleic acid complex (BAMLET). α-Lac’s specificity in exerting less damages to normal cells compared to tumor cells has distinguished it among other proteins25. Furthermore, α-Lac, as the second whey protein, improved solubility of α-Lac than caseins, presence of binding sites for hydrophobic ligands, biocompatibility, and biodegradability make α-Lac a suitable candidate as a drug delivery agent. α-Lac is considered less immunogen compared to other major milk proteins due to eliciting lower levels of IgE. Besides, there is no dominant epitope in α-Lac in the case of T cells’ responses34. In the present study, we aimed to evaluate α-Lac’s potential as a carrier for chemotherapeutic agents such as DOX or PTX using thermodynamic, spectroscopic, and computational techniques. Furthermore, we are going to answer the question of whether α-Lac is capable of embracing the chemotherapeutic agents and consequently causing the slow release of the drugs and their less toxicity by performing and comparing the cellular test results. The current paper reinforces the importance of performing biophysical and thermodynamic studies before preparation of therapeutic nanocarrier.α-Lac has been applied in preparation of stable nanosystems harboring DOX via self-assemblyn), standard enthalpy change (ΔH°), entropy change (ΔS°), and binding constant (Kb) in one experiment35. The primary titration profile of the binding of DOX or PTX to α-Lac at 25 °C and the theoretical fits are shown in Fig. b values of 1.0×104–1.43×104 have been reported37. These values are similar or a little higher than our obtained Kb values. In general, it can be concluded that larger ligands present lower values of Kb compared to smaller ligands .ITC, a powerful technique to study biomolecular interactions, provides a direct method to determine thermodynamic parameters of binding such as binding stoichiometry (vide infra). Moreover, the number of initial DOX binding sites is clearly smaller than the number of initial PTX binding sites on α-Lac , two initial binding sites for PTX, and formation of PTX assemblies in water. It is of special importance to note that enthalpy of PTX solution in the protein buffer was too high, so it was minimized during numerous tests. To this end, PTX was diluted in the protein buffer and also the amount of ethanol needed to dissolve the PTX was minimized.According to values of ΔS° values, α-Lac:PTX presented greater reduction in entropy compared to α-Lac:DOX complexes. This could be due to protein-protein hydrophobic interactions in the presence of the ligand and alteration in arrangement of water molecules in the presence of PTX assemblies in water. Moreover, α-Lac:PTX and α-Lac:DOX interactions are both spontaneous as implied by ΔG° values. Furthermore, considering the negative values of ΔH° and ΔS° for α-Lac:DOX and α-Lac:PTX complexes, it could be concluded that van der Waals forces, hydrogen bonds, and protonation were the effective driving forces of interactions39.Based on Tm), the standard enthalpy change of denaturation (ΔH°), the standard entropy change of denaturation (ΔS°), and the standard heat capacity change upon α-Lac unfolding (ΔC°p) were calculated according to DSC data and presented in Table Tm, ΔH°, ΔS°, and ΔC°p values for 1:5.5 ratio of α-Lac:DOX complex compared to α-Lac. A higher ΔC°p value indicates that hydrophobic patches of α-Lac in complex with DOX are more exposed than α-Lac40. Moreover, ΔH° is thought to derive from intermolecular interactions between hydrophobic amino acids of the proteins. Thus, a higher ΔH° value resulted from DOX interaction with α-Lac reflects greater exposure of hydrophobic patches of α-Lac, which could cause hydrophobic interactions between α-Lac molecules and induce formation of α-Lac assemblies. Furthermore, a higher and positive value of ΔH° following interaction of DOX and α-Lac implies that α-Lac denaturation is an endothermic process.Heat denaturation was studied to determine the effect of DOX or PTX on the stability of α-Lac using DSC. Excess heat capacity as a function of temperature is shown in DSC thermograms Fig. . Furtherm value at 1:5.5 ratio of α-Lac:PTX complex than α-Lac. However, the DSC thermogram of α-Lac and PTX interaction  versus wavelength (nm). As presented in the diagram, with added DOX or PTX the CD spectral profile of α-Lac underwent significant changes. According to the CD data (presented in Table vide infra).Far-UV CD was performed to monitor the secondary structural changes of α-Lac upon interaction with DOX or PTX. The diagram presented in Fig. 41.Furthermore, secondary structural changes induced by DOX or PTX were concentration dependent and increased when measured at higher concentrations. This could indicate the perturbation of the secondary structure of α-Lac by DOX or PTX. There were no perceivable changes in the contents of parallel or anti-parallel β-sheets and β-turns. The extent of changes was also minor in the case of HAS interaction with DOX or PTX and β-lactoglobulin interaction with DOX, in line with our results42. This indicates less or no participation of TRP in the interaction of DOX with α-Lac, which agrees with our simulation studies performed by docking. In fact, according to the docking studies, DOX does not interact with α-Lac’s TRPs. However, α-Lac:PTX systems showed changes in 295 nm, which indicates greater participation of TRP. Accordingly, our docking simulation studies showed greater involvement of TRP 60 during interaction of PTX with α-Lac. Interestingly, the 1:2 ratio of α-Lac:PTX caused negative ellipticity in 295 nm while the 1:5.5 ratio of α-Lac:PTX resulted in a highly positive ellipticity in the same wavelength. As a matter of fact, in the presence of 20 µM PTX, the chemical nature of TRP microenvironment is such that left-handed circularly polarized light (L-CPL) is absorbed to a lesser extent than right-handed circularly polarized light (R-CPL), which leads to appearance of a negative peak in 295 nm. However, higher concentrations of PTX, which causes higher absorbance of L-CPL compared to R-CPL brings about the positive peak43.The near-UV CD spectra of 1:2 and 1:5.5 ratios of α-Lac:DOX and α-Lac:PTX complexes, shown in Fig. 42. Moreover, when there is TYR- TRP sequence of amino acids, a weak peak at 295 nm is observed which could be seen in the case of DOX and 20 µM PTX44. However, higher concentrations of PTX provided huge changes in 295 nm possibly due to generation of another binding site in the vicinity of TRP 60, which was confirmed by our computer simulations.In the case of DOX, more intense ellipticity in 260 nm could be attributed to an electron transfer between oxygens containing DOX and the TYR of α-Lacversus temperature in the presence and absence of various concentrations of DOX or PTX is shown in Fig. 45. Denatured fraction of α-Lac (fd) can be calculated according to equation . As a first approximation, protein denaturation can be approached using the two state theory (Native ↔ Denatured), and the equilibrium constant (K) value was calculated by equation  for denatured α-Lac was determined46:Furthermore, using equations  and 4),,4), the ∆G° as a function of temperature is plotted in Fig. ∆G° linear variation with temperature over the unfolding region. In order to estimate the conformational stability at room temperature (∆G°25), it is necessary to assume that a linear dependence is valid until 25 °C.Furthermore, According to the results presented in Table 1 of 0.91 and τ2 of 3.53 ns with amplitudes of 61.76 and 38.24%, respectively, were obtained for α-Lac. Therefore, the average τ of 1.91 ns was calculated according to the equation (2 = 1.273). Furthermore, adding 0, 2.0, and 5.5 μM DOX or PTX to the α-Lac solution did not cause significant changes in the fluorescence lifetime. This implies that the interactions between α-Lac and DOX or PTX are of static nature which means that DOX or PTX bound to α-Lac dissociate from their sites in hydrophobic pockets after a definite time while free DOX or PTX in the solution replace them. This already has been shown in the case of HAS and PTX in which the changes in fluorescence lifetime was slight and the interaction was statistic. The reported results are in line with our findings47. As it will be seen, this finding would be of importance in our cytotoxic studies.Based on the results of fluorescence lifetime measurements, τ48. In the presence of hydrophobic ligands, proteins may form protein assemblies49 and in such case FITC molecules may become close enough for the resonance energy transmission to happen.α-Lac was labeled with FITC and the anisotropy of the labeled protein was measured before and after DOX or PTX binding. The dye to protein ratio was low and the anisotropy of unlabeled protein was 0.26. The results obtained from anisotropy measurements are displayed in Fig. As it can be seen in Fig. Blind and local docking were performed to indicate possible binding sites for ligands in the protein. According to the blind docking results, two and three binding sites on α-Lac were predicted for DOX and PTX, respectively Fig. . As presBased on the results of the modeling, dominant interaction is hydrophobic and hydrogen bond. As indicated in Fig. 50. Generally, the time evolution of RMSD shows the simulation stability. Furthermore, it is considered as a way to evaluate the results of molecular docking. Moreover, RMSD of the protein backbone was calculated relative to the initial structure in the presence and absence of DOX or PTX molecules , solvent accessible surface area (SASA) and the secondary structure  were studied to analyze the structuresles Fig. . Based oSASA is another parameter for investigating the conformational changes of the protein structure which calculated in these simulations. The average values of SASA are 7137, 7162, and 7175 Å2 for α-Lac, α-Lac:DOX, and α-Lac:PTX, respectively. These data show the greater exposure of α-Lac surface to solvent in the presence of DOX and PTX molecules which are agreement with experimental data. Furthermore, secondary structure was analyzed using DSSP program. As shown in Fig. The hydrogen bond analysis was performed to study the interactions in α-Lac:DOX and α-Lac:PTX complexes Fig. . In the The stability of hydrogen bonds was measured by % occupancy Table . Our finFurthermore, the average structures for the last 20 ns of the MD simulation for α-Lac:DOX and α-Lac:PTX complexes were investigated and are presented in Fig. 51. Furthermore, based on our MTT results, the presence of α-Lac could decrease the cytotoxicity of DOX or PTX whether applied in equal or ascending ratios at 24, 48, and 72 h. This could stem from the interactions of DOX or PTX with α-Lac and subsequent reduction of free drug concentrations. A related point to consider is that these interactions are stable until 72 h and could decrease DOX or PTX toxicity up to 72 h which could be an indication of static interaction of α-Lac with DOX or PTX. This reflects the fact that some drug molecules present in the culture media were free and part of them bound to α-Lac molecules after the interactions between α-Lac and DOX or PTX reached their equilibria. In fact, there was an equilibrium between α-Lac and free drugs present in the solution, i.e. over time, part of bound drugs dissociate and enter the culture media and part of free drugs replace them in turn. Consequently, free effective DOX or PTX concentrations could be lower in the presence of α-Lac than its absence. Over a time, the free drugs entered the cells and caused their death, which means their concentrations in the culture media has decreased. Therefore, in order to reach an equilibrium again, less drugs were bound to the protein. This could indicate slow release of the drugs which is considered as an important principle in drug delivery. Slow release of drugs could result in less drug injections and consequently less damage to healthy tissues which is very desirable.The cytotoxicity of α-Lac:DOX or α-Lac:PTX was compared with that of the free α-Lac, DOX, and PTX against MDA-MB-231 and T47D cells following 24, 48, and 72 h incubation using MTT assay. Fig. b is higher than that of α-Lac:DOX, it can be concluded that more PTX molecules interacted with α-Lac compared with DOX. In other words, free PTX concentration is smaller in α-Lac:PTX than free DOX concentration in α-Lac:DOX solutions.Besides, α-Lac was more effective in toxicity reduction of DOX or PTX when applied in equimolar ratio. This could be due to the fact that there are enough α-Lac molecules available to interact with DOX or PTX when applied in equal ratio while less α-Lac molecules are present when DOX or PTX were applied at ascending ratio. Thus, additional free DOX or PTX molecules are available to penetrate the cells when applied ascending stoichiometries. Interestingly, α-Lac was more efficient in reducing PTX toxicity than DOX, which is in line with our thermodynamic results. As reported in thermodynamics section, since there are two binding sites for PTX on α-Lac and α-Lac:PTX’s KApoptosis induction by α-Lac alone, DOX alone, PTX alone, and α-Lac:DOX or α-Lac:PTX complexes were evaluated by flow cytometry. In the current study, different staining of viable and apoptotic cells by AO and EB were applied to assess apoptosis by flow cytometry. Representative flow cytometric outputs of treated MDA-MB-231 cells after 48 h incubation are displayed in Fig. In the current study, it was suggested that α-Lac may serve as a novel and appropriate carrier for chemotherapeutic agents DOX or PTX as supported by thermodynamic, spectroscopic, computational, and cellular studies. Based on our studies, van der Waals forces and hydrogen bonds were shown to be involved in the interactions between ligands and α-Lac. Furthermore, it was revealed that the interactions between α-Lac and DOX or PTX are of static and spontaneous nature. According to the results, one binding site for DOX and two binding sites for PTX were detected. Based on spectroscopic and computational studies, it was revealed that TRP 60 of α-Lac is engaged in interaction with PTX. While, no TRP is involved in α-Lac’s interaction with DOX. In addition, α-Lac interaction with DOX or PTX led to greater exposure of α-Lac hydrophobic patches. Consequently, protein-protein interactions were facilitated by α-Lac:ligand interactions. Besides, DOX caused higher thermal stability of α-Lac while PTX resulted in its lower thermal stability. Interestingly, α-Lac could lower the cytotoxicity of DOX or PTX as revealed by MTT and apoptosis assays. It seemed that, α-Lac interactions with DOX or PTX could result in their slower release that is desirable in drug delivery. Since the interaction of α-Lac with DOX or PTX was static in nature, the formed complexes were stable up to at least 72 h. However, α-Lac presented more potential in reducing PTX toxicity compared to DOX as revealed by cytotoxicity studies and confirmed by thermodynamic findings.α-Lac from bovine milk-type III-calcium depleted (L 6010), -2,5-dephenyltetrazolium bromide (MTT), dialysis membranes , acridine orange, Ludox®, penicillin, and tris(hydroxymethyl) aminomethane (Tris) were obtained from Sigma-Aldrich. Fetal bovine serum (FBS), RPMI 1640 culture medium, and trypsin were purchased from GIBCO. DOX-HCl, ethidium bromide, ethylenediaminetetraacetic acid (EDTA), and dimethyl sulfoxide (DMSO) were from Merck. Fluorescein 5-isothiocyanate (FITC) and PTX were obtained from Thermo-Fisher Scientific  and Stragen Pharma, respectively. Flasks, petri dishes, and 96-well plates were purchased from Nunc (Denmark).2 at 37 °C. MDA-MB-231 and T47D cells were routinely sub-cultured using trypsin/EDTA in phosphate buffered saline (PBS) solution52.MDA-MB-231 and T47D breast cancer cell lines were received from National Cell Bank of Iran . Both cell lines were cultured in RPMI 1640 medium supplemented with 10% heat-inactivated fetal bovine serum (FBS), 500 μg/ml penicillin, and 200 μg/ml streptomycin. The cells were incubated in a humidified atmosphere (95%) with 5% CO2+, was applied during the thermodynamic, spectroscopic, and cellular experiments as a solvent for α-Lac. Furthermore, ethanol was used to dissolve PTX in all the experiments. It is important to note that, the amount of ethanol used in all the experiments was less than 3% (v/v) which is lower than maximum 20% (v/v) capable of eliciting conformational changes in the α-Lac, as reported in the published literature54. Besides, the authors are willing to emphasize that the effect of the amount of 3% (v/v) ethanol was measured on α-Lac structure in isothermal titration and differential scanning calorimetries and detected no change which has been documented in our previous publication31. In addition, it is necessary to emphasize that during all the spectroscopic, thermodynamic, and cellular experiments, the protein was soluble and never precipitated after forming complexes (vide infra) with DOX or PTX.In the current study, 20 mM Tris buffer, the most common buffer used for α-Lac, containing 3.5 mM EDTA to remove the bound Ca55. Moreover, in order to exclude the probable pH changes due to titration with DOX or PTX solutions, i.e. the highest amount was 15% and 6% (v/v) of the total volume at the end of titrations, respectively, the authors measured the pH and detected no change.In the case of thermodynamic and spectroscopic studies, it should be taken into account that, since there is a decrease of 0.025 unit in pH of Tris buffer for 1 °C of temperature raise , the total amount of changes in pH during temperature raise from 25 to 90 °C would be nearly 1.5 unit (pH 5.5). However, according to the published literature, α-Lac is resistant to pH fluctuations up to pH 4Heat flow was measured using isothermal titration calorimetry (ITC) to determine type of the interactions. ITC assays were performed on a VP-ITC Microcalorimeter  system at 25 °C. The 290 µM α-Lac, loaded in the sample cell, was titrated with 3.4 mM DOX while 50 µM α-Lac was titrated with 250 µM PTX in pH 7.0. In the both cases, the initial volume of α-Lac was 1.8 ml. The reference cell was loaded with 20 mM Tris buffer (pH 7.0). In each experiment, all the solutions were degassed under vacuum for 3 min before loading in the ITC cells. The measurements were started by injecting 5 μl of the titrant into the reactor, which were proceeded by 29 successive injections. These injections were performed at a rate of 10 µl per 20 s with 3 min spacing time between them. To correct the heat effects due to mixing and dilution, the heat effects due to PTX and DOX in protein buffer and the effect of ethanol as PTX’s solvent under identical condition (temperature and timing) were subtracted from the heat of α-Lac:DOX and α-Lac:PTX titrations. The experimental data were analyzed and fitted using MicroCal Origin software to calculate the binding parameters.Differential scanning calorimetry (DSC) measurements were performed on a differential scanning calorimeter (N-DSC II) supplied by Calorimetry Sciences Corp . A pressure of 2 atm was applied to prevent the formation of air bubbles and avoid changes in the sample volume. The thermograms of 1:5.5 molar ratio of α-Lac:DOX and 1:0.5 and 1:5.5 molar ratios of α-Lac:PTX systems were recorded over a temperature range of 15-60 °C with a heating rate of 2 °C/min. The reference cell was loaded with Tris buffer. All experiments were performed in a solution of 1.5 mg/ml α-Lac in Tris buffer containing EDTA. The sample scans were prepared for analysis by subtracting the buffer baseline followed by calculation of thermodynamic parameters from thermograms using CpCalc software (version 2.1) supplied by the manufacturer.2+. Afterwards, α-Lac was titrated with small increments of DOX or PTX stock solutions . At the end of titration, molar ratios of 1:2.0 and 1:5.5 were obtained for both α-Lac:DOX and α-Lac:PTX. The α-Lac:DOX and α-Lac:PTX solutions were vortexed for 15 s followed by incubation for 4 min to allow the formation of α-Lac:DOX and α-Lac:PTX complexes prior to CD measurements. Due to DOX and PTX absorption in the wavelength range of experiments, it was necessary to do a correction. Therefore, CD spectra were corrected by subtracting the spectra of DOX or PTX from α-Lac in the presence of various concentrations of DOX or PTX57. The CD results were expressed in terms of molar ellipticity ([θ]) in deg cm2 dmol−1 according to the following equation:55, θ is the CD signal in degrees at each wavelength, c is the α-Lac concentration in mg/ml and l is the length of the lightpath (cm). CDSD and CDNN software analysis was applied to deconvolute the CD spectra to determine the tertiary structure and the content of various secondary structure elements.Far-UV (195-260 nm) and near-UV (260-320 nm) circular dichroism (CD) spectra were measured to evaluate changes in the secondary and tertiary structures, respectively, of α-Lac induced by DOX or PTX. The CD spectra were recorded at room temperature (25 °C) by an Aviv 215 Spectropolarimeter  using a cuvette of 1 mm path length for far-UV and 2 mm path length for near-UV measurements. Apo α-lac was formed by dissolving α-Lac  in Tris buffer (20 mM) containing EDTA (3.5 mM) to remove the bound Ca46. Briefly, thermal denaturation curves of 10 μM α-Lac solution in the presence and absence of 2.0-fold and 5.5-fold concentrations of DOX or PTX  were obtained by Carry Eclipse  spectrofluorimeter, at excitation and emission wavelengths of 280 and 337 nm, respectively. α-Lac and the drugs mixed solutions in Tris buffer were vortexed for 15 s, followed by 4 min incubation. Afterwards, α-Lac solution in complex with DOX or PTX was scanned in the temperature range of 20 to 95 °C. Temperature was raised 2 °C per min and the data were recorded every 1 min. The fluorescence intensity was corrected according to the equation .Fluorescence lifetime measurements were performed using an Edinburgh Instruments (FLS 920) spectrofluorimeter in laser mode at excitation and emission wavelengths of 255 and 337 nm, respectively. The spectrofluorimeter was equipped with a temperature-controlled cell connected to a circulating water bath. Fluorescence lifetime of 10 µM α-Lac in 20 mM Tris buffer (pH 7 and containing EDTA) in the presence and absence of 2.0- and 5.5-fold concentrations of DOX or PTX were measured. Using a double-exponential decay function of the decay profiles with F900 analysis software, the lifetime values were determined from the reconvolution fit analysis. When applying the reduced χi of the ith component lifetime τFi, the average fluorescence lifetime (τF) was calculated since TRP and α-Lac presented two different lifetime values59:Furthermore, according to the following equation and using the amplitude α48. In brief, α-Lac was dissolved in Tris buffer (pH 7.0) and reacted with FITC in a darkened lab. The FITC was first dissolved in a few drops of DMSO, then made up to volume in pH 7.0 Tris buffer. The sample was left on a shaker to react for 8 h at room temperature in the dark. Free unreacted dye was separated from the reaction mixture by repeated dialysis against 0.01 M buffer. Dialysis membranes  were prepared according to standard methods and immediately used. Dialysis was considered complete when free FITC fluorescence in the outer solution was no longer detectable.α-Lac labeling with FITC was performed according to published proceduresFluorescence spectra and steady-state emission anisotropies of all α-Lac samples were measured with Cary Eclipse fluorimeter . Anisotropy was measured with a manual polarizer and the G factor correction was done manually, with Ludox® as the reference fluorophore. The labelled protein was then excited at 490 nm and the parallel and perpendicular emissions were monitored at maximum emission of 512 nm with a manual polarizer. The anisotropy measurement of the protein was then repeated with DOX or PTX. DOX is fluorescent and it absorbs light at 490 nm but has no fluorescence emission at 512 nm and shows no interference with the anisotropy measurements.60. Blind docking was used to predict preferred orientation of ligands and binding affinities. Crystal structure of α-Lac was obtained from RCSB protein data bank (1HFZ PDB code). Afterwards, hydrogen atoms were added to the protein structure and initial files were prepared by MGL tools (1.5.6rc3 version)61. Furthermore, DOX and PTX structures were obtained from PubChem (https://pubchem.ncbi.nlm.nih.gov/) and ChemSpider (http://www.chemspider.com/), respectively. Besides, Kollman and Gasteiger charges were assigned to α-Lac and ligand molecules, respectively. Moreover, a grid volume that was big enough to cover the entire surface of the protein was applied using a grid spacing with value of 1 Å for blind docking. Docking results were clustered and the best mode was selected to use for local docking in which a Grid box of 30×30×30 points was used with a spacing of 1 Å. Exhaustiveness was set on 20 and the root mean square (RMS) cluster tolerance was set to 2.0 Å. For other docking parameters, default values were applied. α-Lac was set to be rigid while DOX or PTX molecules were set to be flexible through both blind and local docking. The graphical images representing the best pose were prepared using MGL tools. Besides, LigPlot+ v.1.4.5, was employed to present schematic two dimensional representations of protein-ligand interactions using the PDB files as input62.Molecular docking was performed to study the interactions between α-Lac and DOX or PTX using AutoDock Vina 1.1.2 program63. The MD simulations were carried out with the GROMACS package (Version 5.1.3)64. Furthermore, the force field parameters for DOX and PTX were generated using antechamber module with the general AMBER force field (gaff), while the ff14SB force field was used for α-Lac66. The starting structures of α-Lac:DOX and α-Lac:PTX complexes were put in a periodic rectangular box with a closeness parameter of 12 Å. Then, TIP3P water molecules filled the simulation boxes67. Sodium counter ions were added to the systems to preserve the electroneutrality condition. Moreover, the particle mesh Ewald (PME) method was used to treat the long-range electrostatic interactions68. Besides, the SHAKE algorithm was used to constrain all bonds involving hydrogen atoms69. In addition, the initial velocities were taken from a Maxwell–Boltzmann distribution at a temperature of 300 K. Moreover, a cutoff distance of 10 Å was used to truncate van der Waals interactions in each simulation. The equations of motion were integrated using the leap frog algorithm70 with a time step of 2 fs. For each system, at first, the energy minimization was performed in 5000 steps, using the steepest descent method to remove bad contacts. During the minimization, position restraints with force constants of 1,000 kJ mol−1 nm−2 were considered on all heavy atoms of the α-Lac, DOX, and PTX molecules. Following the energy minimization, heating step was performed from 0 to 300 K through an NVT ensemble MD simulation for 1 ns. Afterwards, 1 ns equilibration was carried out at a constant temperature (300 K) and pressure (1 bar). The control parameters such as total energy, density, temperature, and RMSD were checked during the equilibration step, showing that the equilibration time is appropriate for performance of the next step. Finally, a 100-ns production run was carried out in the NPT ensemble. In NPT MD simulations, the temperature and pressure were controlled by the Nose´–Hoover thermostat71 and Parrinello–Rahman barostat72, respectively. Moreover, the coupling time of 0.1 and 1.0 ps was used for thermostat and barostat, respectively. Totally, three MD simulations were done (α-Lac:DOX and α-Lac:PTX complexes and free α-Lac). The atomic coordinates were saved every 25 ps for analysis. Moreover, molecular graphics were made with the PyMOL program .The best docking poses of α-Lac:DOX and α-Lac:PTX complexes were considered as the initial structures for MD simulations. Restrained electrostatic potential (RESP) charges were assigned to the DOX and PTX molecules using the antechamber module from the AmberTools 14 suite52.The cytotoxicity of α-Lac, DOX, PTX, α-Lac:DOX, and α-Lac:PTX was determined using MTT assay. In this assay, harvested exponentially growing MDA-MB-231 and T47D cells were plated in 96-well plates. Afterwards, the cells were treated with freshly prepared solutions containing various concentrations of α-Lac alone (1-1000 μM), DOX alone , PTX alone , and α-Lac  along with DOX or PTX  with equal  and ascending ratios . The corresponding control cells were also considered in the absence of any kind of treatments. The solutions were vortexed before treatment in order to trigger the interaction between α-Lac and DOX or PTX. After incubation for 24, 48, and 72 h, 10 μl of MTT (5 mg/ml) dissolved in PBS was added to each well followed by incubation for 3 h. Then, 100 μl DMSO was added to each well to dissolve the formazan crystals formed by viable cells. Finally, the absorbance of each well was measured at a test wavelength of 570 and a reference wavelength of 630 nm using an Elisa reader spectrophotometer . Cell viability was calculated by comparing absorbance of the treated cells with that of the control cells, which conventionally was 100%. The results were presented as means of three independent experiments74. Following incubation for 24 or 48 h, the treated cells were trypsinized and pelleted. Afterwards, they were resuspended in 1 ml PBS and stained with AO and EB with the final concentration of 0.1 and 0.25 mM, respectively. All samples were analyzed by CyFlow Space  followed by data analysis using FloMax software.Apoptosis induction in absence and presence of α-Lac alone (1 and 2 μM), DOX alone (1 and 2 μM), PTX alone (1 and 2 μM), and α-Lac (1 and 2 μM) along with DOX or PTX (1 and 2 μM) with equal (1:1 and 2:2) and ascending (1:2) ratios was detected and quantified by flow cytometric analysis of treated MDA-MB-231 and T47D cells stained with acridine orange/ethidium bromide (AO/EB) according to the published proceduresp value of <0.05 was considered statistically significant.Statistical analyses were performed using SPSS version 16.0 statistical software . One-way analysis of variance (ANOVA) and Tukey’s post-hoc testing were applied to compare means for continuous variables. A two-tailed Supplementary information


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