Frontiers in Pharmacology

Diarrhea is the major side effect of first- and second-generation ErbB tyrosine kinase inhibitors (TKI), the mechanism of which remains incompletely understood. The current studies were carried out over the time frame that ErbB TKIs usually initiate diarrhea. We report in Caco-2/bbe cells that exposure of ErbB TKIs, but not non-ErbB TKIs for six days at clinically-relevant concentrations significantly reduced the expression of DRA and inhibited apical Cl-/HCO3-exchange activity. The ErbB TKIs decreased DRA expression through an ERK/Elk-1/CREB/AP-1 dependent pathway. The blockade of ERK phosphorylation by ErbB TKIs decreased the phosphorylation of Elk-1 and the amount of total and p-CREB, and reduced the expression of C-Fos, which is part of the AP-1 complex that maintain DRA expression. Altogether, our studies demonstrate that ErbB TKIs decrease expression and activity of DRA, which occurs over the time frame that these drugs clinically cause diarrhea, and since DRA is part of the intestinal neutral NaCl absorptive process, the reduced absorption is likely to represent a major contributor to the ErbB TKI-associated diarrhea.

BackgroundPrevious work has shown considerable laboratory variability of Biopharmaceutics Classification System (BCS) classification, efflux ratio in intestinal cell lines and cytochrome P450 (CYP450)-metabolism pathways. Such variability and inconsistency create uncertainty in predictions of human clinical pharmacokinetics and the pharmacokinetic optimization process and is a problem when developing corresponding in silico methods. Objectives and MethodologyOne objective of the study was to quantify the degree of laboratory inconsistency for BCS II-classing, MDR-1 and CYP3A4 substrate specificity (substrate/non-substrate). Another objective was to predict BCS II-classing, MDR-1 and CYP3A4 substrate specificity using in silico methodology and compare results to laboratory data/classifications. Results and Discussion27 BCS II-classified drugs (with non-contradictory BCS-classing in various sources) were found. 17 (63 %) had an in vivo fraction absorbed (fa) of [≥]90 % and belong to in vivo BCS I. With in silico methodology, 74 % correct BCS-classing was reached for the same set of compounds. The mean prediction error for fa was 1.2-fold. MDR-1 and CYP3A4 substrate specificities were collected for 346 and 808 compounds, respectively. For MDR-1, 143 of the compounds had reported data in at least two studies, and out of these, 49 (34 %) and 18 (13 %) had contradictory (reported as both substate and non-substrate) and uncertain substrate specificities, respectively. For CYP3A4, 42 (9.8 %) out of 427 compounds showed inconsistency between laboratories. With in silico methodology, MDR-1 and CYP3A4 classification predictions were incorrect for 13 and 15 % of compounds. ConclusionThe results show considerable variability/inconsistency for BCS II-classing (63 % inconsistency between BCS II-classing and in vivo fa) and MDR-1 (34 % inconsistency between sources) and CYP3A4 (10 % inconsistency between sources) substrate specificities. Corresponding estimates obtained with in silico methodology are 22, 13 and 15 %, respectively, demonstrating the power and applicability of such technology.

IntroductionIntrinsic hepatic metabolic clearance (CLint) measured with human hepatocytes, apparent intestinal permeability (Papp) obtained using the Caco-2 model, unbound fraction in plasma (fu) and blood-to-plasma concentration ratio (Cbl/Cpl) are commonly used for predicting the hepatic clearance (CLH) and oral bioavailability (F) of drug candidates in humans. The primary objective was to select drugs whose in vitro hepatocyte CLint, Caco-2 Papp, fu and Cbl/Cpl have been measured in various laboratories and studies, and estimate correlation coefficients (R2) for predicted and observed F and log plasma CLH. Secondary aims were to estimate the laboratory/study variability and its impact on predictions and to compare results to in silico and animal model-based predictions. Materials and MethodsA literature search was done in order to find unbound hepatocyte CLint, (and corresponding predicted in vivo CLint), Caco-2 Papp, fu and Cbl/Cpl data. Compounds with multiple measurements for the four assays, without significant in vivo solubility/dissolution limitations and with known in vivo CLH and F, were selected. Min, max and mean estimates were used in the analysis. Results and DiscussionThirty-two compounds with data (in total 561 estimates) produced by 21 major pharmaceutical companies and universities met the inclusion criteria. The predicted vs observed R2 for log mean CLint, log mean CLH and mean F were 0.32, 0.08 and 0.20, respectively. Exclusion of atenolol increased the R2 for CLH to 0.20. R2-values were considerably lower than those presented in many studies, which seems to be explained by selection bias (choosing favorable reference values). There was considerable interstudy variability for measured and predicted CLint (80- and 1,476-fold mean and max differences, respectively) and measured fu (6.6- and 50-fold mean and max differences, respectively). For F, higher predictive performance was found for in silico (Q2=0.58; head-to-head) and animal in vivo models (R2=0.30). ConclusionThe combination of data from many laboratories and the use of mean values resulted in reduced selection bias and predictive accuracy. Overall, the predictive accuracy (here R2) for log CLint, log CLH and F was low to moderately low (0.08-0.32). The halved R2 compared to individual studies where high performance was demonstrated seems to be explained be selection bias (enabled by large data variability). Animal in vivo models, and in particular, in silico methodology, outperformed in vitro methodology for the prediction of F in man.

BackgroundBlood-brain barrier permeability (BBB Pe) and unbound brain-to-plasma concentration ratio (Kp,uu,brain) are relevant parameters describing the brain uptake potential of compounds. BBB efflux by transporter proteins, mainly MDR-1 and BCRP, is an essential factor determining Kp,uu,brain. Kp,uu,brain-values are commonly estimated in vivo in rats and monkeys and predicted using in silico methodology. Such estimates can be used to predict corresponding human clinical values. ObjectiveThe objective of the study was to evaluate the reliability and applicability of human clinical Kp,uu,brain-data for understanding and predictions of brain uptake in man. MethodologyKp,uu,brain in rats, monkeys and humans, measured and in silico predicted MDR-1 and BCRP substrate specificities and in silico predicted passive Pe were used for the analysis. In silico predictions were done using the ANDROMEDA by Prosilico ADME/PK-prediction software. Results and DiscussionRat and monkey Kp,uu,brain-values were highly correlated (R^2=0.74; n=17). Based on this finding a correlation between rat and human Kp,uu,brain was expected. However, no correlation between rat and human Kp,uu,brain was found (R^2=0.01; n=13). There was no (as also anticipated) correlation between passive Pe and human Kp,uu,brain (R^2=0.04; n=16) and compounds with measured or predicted efflux did not have lower Kp,uu,brain than compounds without efflux. The compound with highest Kp,uu,brain in man (2.8) is effluxed and predicted to have high passive Pe and has no apparent efflux at the rat BBB. The MDR-1 substrate with highest Kp,uu,brain in rat (2.4) has very low Kp,uu,brain in man (0.15) is predicted to have high passive Pe. ConclusionResults indicate that available human Kp,uu,brain-data are too uncertain to be applicable for validation of predictions and understanding of clinical brain uptake of drugs and drug candidates.

IntroductionConformal prediction (CP) methodology sits on top of machine learning methods and produces prediction confidence intervals that depend on how "strange" (non-conforming) test compounds are compared to training set compounds. CP has previously been successfully applied for prediction of steady-state volume of distribution (Vss) in humans, with 69 % of observations within the prediction interval at a 70 % confidence level. We have developed CP models for a variety of human pharmacokinetic (PK) parameters and validated their predictive accuracy (predicted vs observed estimates), but not validated prediction confidence intervals for them. The main objective of this study was to predict 70 % confidence intervals for Vss, unbound fraction in plasma (fu), intrinsic metabolic clearance (CLint), fraction absorbed passively (fa,passive) and maximum fraction dissolved (fdiss) for a variety of compounds in man and investigate the consistency between prediction intervals and observed/measured values. MethodologyCP models featured in the ANDROMEDA software by Prosilico were used for prediction of 70 % confidence intervals of Vss, fu, CLint, fa,passive and fdiss for compounds from different chemical classes and with broad physicochemical variety and for small drugs marketed in 2021. Results70 % prediction confidence intervals for 217, 117, 117, 89 and 89 compounds were produced for Vss, fu, CLint, fa,passive and fdiss, respectively. 78 % (expected 70 %) of observed data were within 70 % confidence intervals for the parameters. 70 % of predictions of Vss, fu, CLint fa,passive and fdiss are expected to have errors of maximally 2-, 4- and 6-fold and 7 and 12 %, respectively, which is in line with prediction errors. These findings validate the CP methodology. ConclusionIn conclusion, the results further validate CP models and confidence intervals of ANDROMEDA for prediction of human PK.

The main objective of this study was to evaluate and compare the performance of 5 PK software, ANDROMEDA by Prosilico 2.0, and 4 free, web-based prediction tools, PkCSM, Swiss ADME, ADMETlab 3.0 and DruMAP 2.0, for predictions of fraction absorbed (fa) and unbound (fu) in humans. Sets with compounds with available and undisclosed estimates were selected (n=140). The risk that test compounds have been used in training sets for model building (and thereby, influenced and exaggerated the predictive performances) was minimized. At least, these were not included in training sets for ANDROMEDA. One set consisted of compounds that have not been marketed and for which pharmacokinetic information has not been publically disclosed. Quantitative and qualitative evaluations and comparisons were done. For both fa and fu, ANDROMEDA was clearly more accurate and balanced than the others, with higher Q2 (0.69 vs 0.35 for fa; 0.94 vs 0.62-0.76 for fu), lower mean errors (15 % vs 28 %; 2.3- vs 4.1- to 36-fold), lower maximum errors (54 % vs 92 %; 10- vs 30- to 524-fold), more correct predicted classes (70-77 % vs 13-54 %), no failed, inconclusive or poor predictions (as found for 3 of the other software), wider application range, and minimal skewness at low values. It had intercepts on fa- and fu-prediction axes that were ca 1/3 and 1/175 to 1/23 compared to those found for the other software, which is of particular importance. Two software, PkCSM and Swiss ADME, were considered inappropriate, whereas ADMETlab took an intermediate performance position. Apparently, DruMAP was second best performing software. Overall, there was poor performance overlap between the software (7-24 %), with many contradictory predictions. Advantages with ANDROMEDA suggest that this is the software of choice for those that desire adequate predictions of fa and fu in humans and estimates of certainty. The findings are of particular interest for the 3R-process.

There has been a lot of interest and publicity regarding the use of a complex biophysical model within drug development for predicting the TdeP risk of new compounds. Throughout the development of the complex model numerous groups have shown that a simple linear mechanistic model explains the predictive behaviour of complex mechanistic models. That is the input-output relationship is almost linear even when complex kinetic assays are used. We hypothesized that given this linear relationship that scientist would be able to predict the outcome of the biophysical model. The objective of this pilot study was to assess the feasibility of such an analysis but also assess the initial degree of correlation. A set of 15 compounds with diverse ion-channel blocking against 4 ion-channel currents, IKr, ICaL, INa and INaL, was generated. Safety pharmacologists across numerous companies were approached and asked to categorize the TdeP risk of these compounds using only the % block depicted via a bar chart into one of 3 categories: Risk, No-risk or Unsure. 12 scientists participated in the study, of which 11 correlated strongly with the model (11 person ROC AUC range: 0.86-1, 7 scientists had a value >0.9). The combined prediction of all scientists also correlated strongly with the model. These results highlight that the linear input-output relationship can indeed be predicted by the scientist. A future study exploring the degree of correlation with a wider group of scientists and wider set of compounds would be required to get a more precise estimate of the correlation. We hope this initial exploratory study will encourage the community to pursue this idea.Competing Interest StatementThe authors have declared no competing interest.View Full Text

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSIntroductionC_ST_ABSIn vitro measurements and predictions of human clinical pharmacokinetics (PK) are sometimes hindered and made impossible due to factors such as extensive binding to materials, low methodological sensitivity and large variability. MethodsThe objective was to find compounds out of reach for in vitro PK-methods and (if possible) predict corresponding human clinical estimates using the ANDROMEDA by Prosilico software. In vitro methods selected for the investigation were human microsomes and hepatocytes for measuring and predicting intrinsic hepatic metabolic clearance (CLint), Caco-2 and Ralph Russ canine kidney cells (RRCK) cells for measuring apparent intestinal permeability (Papp) for prediction of fraction absorbed (fa), plasma for measurement and estimation of unbound fraction (fu), and water and buffers for measuring solubility (S) for prediction of in vivo dissolution potential (fdiss). Results and ConclusionAs many as 329 non-quantifiable in vitro PK-measurements for 300 compounds were found in the literature: 191 for CLint, 101 for Papp, 11 for fu and 26 for S. ANDROMEDA was successful in predicting all corresponding clinical PK-estimates for the selection of compounds with non-quantifiable in vitro PK, and predicted estimates (1.6-fold median prediction error; n=159) were generally in line with observed in vivo data and results/problems at in vitro laboratories. Thus, ANDROMEDA is applicable for predicting human clinical PK for compounds out of reach for laboratory methods.

IntroductionSolubility/dissolution and permeability are essential determinants of gastrointestinal absorption of drugs. In vitro aqueous solubility (S) and apparent permeability (Papp) are commonly used as measurements and predictors of in vivo fraction absorbed (fa) and BCS-classing in humans. The objective of this study was to explore the relationships between in vitro aqueous S and Dose number (Do) and in vivo fa and in vitro Papp and in vivo fa and the predictive power of in vitro aqueous S, Do and Papp. MethodsIn vitro and in vivo data were taken from studies in the literature and correlated. In vitro S data were produced in various laboratories and with different methodologies. In vitro Papp data were produced using Caco-2 and MDCK cells in various laboratories and Caco-2 and RRCK cells in one laboratory each. Do was estimated as oral dose / (S * 250 mL). Results452 S data and 1480 Papp data were found and used. There was no correlation (R2=0.0) between in vitro log S and Do vs in vivo fa, not even at S<1 mg/L or not for compounds with <90 % and <30 % in vivo fa. A R2 of 0.43 was found between log Caco-2 Papp and in vivo fa. The corresponding R2 for Caco-2 from one laboratory was 0.65. The interlaboratory R2 for the Caco-2 model was 0.48. R2-estimates for Caco-2 vs MDCK and Caco-2 vs RRCK Papp were 0.23 and 0.21, respectively. Discussion and ConclusionAqueous S appears to have no predictive value of in vivo fa in humans, not even at low S or after correction for dose. The shows that one should not base human biopharmaceutical predictions based on aqueous S. Log Caco-2 Papp explains about half of the variance of in vivo fa in humans. The poor correlations found between Caco-2 and the two other Papp-models (MDCK and RRCK) demonstrate considerable methodological differences. The unexplained variance does not appear to be explained by S and dose, but rather by in vitro-in vivo difference in permeability and poor/absent relationship between in vitro S and in vivo dissolution potential.

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSIntroductionC_ST_ABSVarious in vitro methods are used to measure absorption, distribution, metabolism and excretion/pharmacokinetics (ADME/PK) of candidate drugs and predict and decide whether properties are clinically adequate. MethodsObjectives were to evaluate variability within and between laboratories for commonly used human in vitro ADME/PK methods and to explore whether reliable thresholds may be defined. The literature was searched for in vitro data for intrinsic metabolic clearance (hepatocyte CLint), apparent intestinal permeability (Caco-2 Papp), efflux ratio (Caco-2 ER), solubility (S) and BCS-class, and corresponding clinical estimates. In vitro ADME/PK data for three example drugs (atenolol, diclofenac and gemfibrozil) were used to predict human in vivo ADME/PK and investigate whether these would pass a compound selection process. Results and ConclusionsInterlaboratory variability is considerable, especially for fu, S, ER and BCS-classification, and on average about twice as high as intralaboratory variability. Approximate mean interlaboratory variability for CLint, Papp, ER and fu (3- to 3.5-fold) appears to be about 2- to 3-fold higher than corresponding interlaboratory variability. Mean and maximum interlaboratory range for CLint, Papp, ER, fu and S are approximately 5- to 100-fold and 50- to 4500-fold, respectively, with second largest range for fu and largest range for S. For one drug, laboratories produced almost 1000-fold different CLint * fu-values. It appears difficult/impossible to set clear clinically useful thresholds, especially for CLint, ER and S. Poor in vitro-in vivo consistency for S and BCS-classification and large portions of compounds out of reach for Caco-2 and conventional hepatocyte assays are evident. Predictions for reference compounds are consistent with inadequate in vivo ADME/PK. Ways to improve predictions and compound selection are suggested.

Non-compartmental analysis (NCA) is a popular strategy for obtaining estimates of pharmacokinetic parameters, while requiring both minimal structural assumptions, and limited input by the analyst. As typically applied, its scope and depth are constrained by its statistical simplicity. Embedding the NCA within a hierarchical generalized additive model (HGAM) may facilitate the simultaneous analysis of data from multiple subjects, estimation of covariate effects in one stage, and implementation of censored responses, similarly to the capabilities of nonlinear multilevel models as widely applied in pharmacometrics. HGAM is an interesting extension to multilevel linear models that allows the effects of predictors to be implemented as smooth functions, which has been widely implemented in various disciplines to nonlinear trends, including for longitudinal data. This approach extends the capability of previous implementations of spline-based methods applied to NCA, within an accessible workflow in open software. Application of HGAM to two example datasets, one describing oral drug administration, and one describing IV and oral drug administration with categorical covariates and censoring, illustrates the overall approach, including parameter estimation, visualization and model checking, and uncertainty quantification. A Bayesian approach to estimation facilitates interpretable expressions of the uncertainty in individual parameters, population parameters, and functions of parameters such as contrasts.

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSPassive blood-brain barrier permeability (BBB Pe), fraction bound to brain tissue (fb,brain) and efflux by transport proteins MDR-1 and BCRP are essential determinants for the brain uptake and disposition of drugs. MethodsThe main objective of the study was to use the software ANDROMEDA by Prosilico to predict passive BBB Pe- and fb,brain-classes and MDR-1- and BCRP-specificities for various classes of antidepressants and for CNS-active small drugs marketed during 2020 and 2021, and then to position them according to a new 2-dimensional Brainavailability-Matrix (8 passive BBB Pe x 4 fb,brain classes, where class 11 has highest and 84 lowest values/brainavailability). Predicted estimates were used, except for cases where measured values were available. Results and ConclusionResults for 53 drugs show that adequate CNS uptake and disposition are achieved for compounds placed in the zones for low, moderate and high brainavailability, despite efflux. They also show that high brainavailability and efflux are common for CNS-active drugs and that modern CNS-active drugs generally have lower brainavailability than older antidepressive drugs. Furthermore, they demonstrate that ANDROMEDA by Prosilico and the new Brainavailability-Matrix are applicable for prediction, optimization and positioning of CNS uptake and disposition of drugs and drug candidates in man.

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSIntroductionC_ST_ABSThe ANDROMEDA software by Prosilico has previously been successfully applied and validated for predictions of absorption characteristics of small drugs in man. The influence of fat food on the gastrointestinal uptake and systemic exposure of drugs have, however, not yet been evaluated with the software. Objective and MethodologyThe main objective was to use ANDROMEDA to predict area under the plasma concentration-time curve ratios in the fed (fat food) and fasted states (AUCfed/AUCfast) for small drugs (including those marketed in 2021) and compare results with corresponding measured clinical estimates. Actual dose sizes were considered. Another objective was to compare the performance of ANDROMEDA vs physiologically based pharmacokinetic (PBPK) modelling and simulations by The Food Effect PBPK IQ Working Group. PBPK results generated using Simcyp and GastroPlus software were based on various physicochemical, in vitro and in vivo data and a decision tree for model verification and optimization. Results and Discussion63 drugs, including 17 new drugs, with observed AUCfed/AUCfast between 0.2 and 5.5 were found and used for this evaluation. Predicted AUCfed/AUCfast had mean and maximum errors of 1.5- and 4.1-fold, respectively, and the predictive accuracy (correlation between predicted and observed AUCfed/AUCfast; Q2) was 0.3. 14 % of predictions had >2-fold error. For 72 % of drugs, food interaction class was correctly predicted. The level of predictive accuracy was overall similar to results obtained with PBPK modelling and simulations, however, with lower maximum error and higher compound coverage. With PBPK models, maximum simulation error was 7.7-fold and 3 highly lipophilic compounds were not possible to simulate. ConclusionThe results validate ANDROMEDA for prediction of fat food-drug interaction size for small drugs in man. Major advantages with the methodology include that prediction results are produced directly from molecular structure and oral dose and are similar to PBPK-simulation results obtained using in vitro and clinical data. Furthermore, ANDROMEDA showed lower maximum errors and wider compound range.

"Revealing the structure of pharmacobehavioral space through motion sequencing" by Wiltschko et al. (2020) has been highly influential in behavioral phenotyping research. In a cohort of nearly 700 mice, the authors demonstrated that Motion Sequencing (MoSeq) could distinguish behavioral effects across a large and diverse set of neuroactive and psychoactive compounds. A central conclusion of the study is that MoSeq syllable features substantially outperform more traditional scalar behavioral features in treatment classification tasks. Although this comparison is not emphasized outside the Results section, the reported advantage corresponds to an increase in classification performance exceeding 50% relative to scalar feature representations. While reproducing parts of the analysis using the publicly available dataset, we found that much of this apparent performance difference can be attributed to differences in preprocessing, classifier selection, and hyperparameter optimization. Under alternative, but comparably standard, analytical choices, the performance gap between scalar features and MoSeq syllables was reduced to approximately 11%. Furthermore, in our reanalysis, the performance advantage of MoSeq syllables became statistically significant primarily in highly dense pharmacobehavioral spaces. These findings do not contradict the utility of MoSeq syllables. Rather, they suggest that the magnitude and generality of their advantage over simpler scalar features may depend strongly on analytical methodology and dataset structure. This distinction is practically relevant, as scalar feature approaches are substantially less computationally demanding and often easier to interpret biologically. Consequently, for laboratories with limited computational resources or for studies focused on specific treatment effects, conventional scalar representations may provide a competitive and more accessible alternative. Our findings highlight the importance of analytical standardization and reproducibility in comparative behavioral representation studies.

1Pharmacometric approaches achieves higher power to detect a drug effect compared to traditional statistical hypothesis tests. Known drawbacks come from the model building process where multiple testing and model misspecification are major causes for type I error inflation. IMA is a new approach using mixture models and the likelihood ratio test (LRT) to test for drug effect. It previously showed type I error control and unbiased drug estimates in the context of two-arms balanced designs using real placebo data, in comparison to the standard approach (STD). The aim of this study was to extend the assessment of IMA and STD regarding type I error, power, and bias in the drug effect estimates under various types of model misspecification, with or without LRT calibration. Two classical statistical approaches, t-test and Mixed-Effect Model Repeated Measure (MMRM), were also added to the comparison. The focus was a simulation study where the extent of the model misspecification is known, using a response model with or without drug effect as motivating example in two sample size scenarios. The IMA performances were overall not impacted by the sample size or the LRT calibration, contrary to STD which had better type I error results with the larger sample size and calibrated LRT. In terms of power STD required LRT calibration to outperform IMA. T-test and MMRM had both controlled type I error. The t-test had a lower power than both STD and IMA while MMRM had power predictions similar to IMA. IMA and STD had similarly unbiased drug effect estimates, with few exceptions. IMA showed again encouraging performances (type I error control and unbiased drug estimates) and presented reasonable power predictions. The IMA performances were overall more robust towards model mis-specification compared to STD. IMA confirmed its status of promising NLMEM-based approach for hypothesis testing of the drug effect and could be used in the future, after further evaluations, as primary analysis in confirmatory trials.

Nutrition is a fundamental aspect in human life development, both from a socio-economic and medical perspective. In recent years, new personalized approaches have been added to the biochemical study of nutrients. These approaches consider both the effect of foods on the body and the role of genes in metabolizing or digesting different nutrients. Although drug-food interactions have been known for decades, there is a lack of studies that address these processes in a comprehensive way, using structural and computational biochemistry techniques. In this paper we develop a method to predict potential interactions between foods and drugs based on the structural similarity between food compounds and medications. Our results have produced a database and an app to consult potential interactions between drugs and foods that we have called FARFOOD. Additionally, we validated two of these potential interactions with widely used drugs (lisinopril and bupropion) through structural docking between the ligand protein and the food compounds that are structurally similar to the drug. Moreover, patient surveys are used in the lisinopril and bupropion cases in addition to allopurinol to assess the possible effects of the potentially interacting foods on the symptoms of the conditions for which the medication is prescribed. In summary, this manuscript presents an interesting computational resource for predictive food-drug interaction analysis.

O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=118 SRC="FIGDIR/small/596117v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@177bd39org.highwire.dtl.DTLVardef@27f63forg.highwire.dtl.DTLVardef@197a43aorg.highwire.dtl.DTLVardef@9f0ad0_HPS_FORMAT_FIGEXP M_FIG C_FIG Flavonoids are natural secondary compounds of plants with a basic composition derived from polyphenols that can produce a plethora of different neurochemical effects, some of which are relevant to anxiety disorders. As such, many flavonoids have been evaluated in behavioral screens in preclinical research on anxiolytics. Given the many different molecular subclasses of flavonoids, the many different molecular targets that have been proposed for these compounds, and the different research priorities that arise in preclinical research, we sought to map the potential of flavonoids as anxiolytics by bibliometric analysis and a meta-analysis of animal tests using these compounds. Bibliometric analysis suggest that the field is highly concentrated on few research groups that are mostly located in the Global South, suggesting the need to improve international collaborations. The themes which emerged in the bibliometric analysis are driven by the exploratory steps of pharmacological research, including finding anxioselective effects and looking for dose-response patterns; this suggests that the field, as a whole, could benefit from more mechanistic and confirmatory research. The meta-analysis showed strong evidence for an anxiolytic-like effect of flavonoids on animal tests, including assays made in rats, mice, and zebrafish (SMD = -1.4398, 95%CI[-1.7319, - 1.1477]). Subgroup analysis suggested that this effect is present in acute treatment (SMD = -1.14, 95%CI[-1.36, -0.93]), but not after chronic treatment (SMD = -1.96, 95%CI[-4.93; 1.01]). For all molecule subclasses included in the study, only isoflavones, glycoside derivatives, and flavanolignans did not show evidence of an anxiolytic-like effect, although the low number of studies including these subclasses is low. We finish with a set of recommendations for preclinical research on the anxiolytic potential of flavonoids.

While initially protective, prolonged neuroinflammation can lead to a disrupted brain microenvironment and neurodegenerative diseases. Microglia cells are the primary regulators of neuroinflammation, with anti- or pro-inflammatory phenotypes. Just as microglia are crucial in neuroinflammation, calcium is a crucial regulator in the activation of the microglia. Intracellular calcium dynamics can be modulated by small, membrane bound chaperone proteins called sigma receptors. Both sigma receptors 1 and 2, which are involved in a wide variety of cellular functions, have been implicated in neurodegenerative diseases, psychiatry, and cancer. We examined the use of sigma-receptor ligands to modulate cytosolic calcium levels in BV2 cells, an immortalized mouse microglia cell line. Immunofluorescence staining detected both sigma 1 and 2 receptors in perinuclear regions and the cell cytoplasm. Our selection of compounds was a mixture of commercially available sigma receptor ligands and compounds synthesized at Southern Illinois University Edwardsville. We used the Fluo-8-AM calcium probe to measure cytosolic calcium concentrations using flow cytometry after 15-, 25- and 35-minute ligand exposure. With 1 {micro}M ligand concentration, we found significantly increased cytosolic calcium levels in BV2 cells after 15-minute exposure. A moderate correlation was determined between sigma receptor 2 selectivity and calcium activity, suggesting a sigma-mediated calcium effect. In addition, we aimed to determine the location of calcium flux by pretreating cells with thapsigargin and EGTA to inhibit both the endoplasmic reticulum and extracellular space of suspected calcium entry. We found all four compounds tested, siramesine, PB-28, cis-8-OMe BBZI, and BN-IX-111-F1 promote calcium entry from the extracellular space, but only siramesine promotes calcium entry from both locations. We tested for a common drug-induced effect called phospholipidosis using HCS LipidTOX phospholipid detection reagent to determine a correlation between calcium activity and a membrane-mediated effect. Only two out of the five compounds tested, PB-28 and amiodarone, resulted in significant phospholipid accumulation at 10 {micro}M treatment, suggesting little to no correlation between calcium levels and phospholipidosis. Further investigations are required to target the exact mechanism of calcium flux and characterize the connection between phospholipidosis and sigma receptors after sigma ligand exposure. Nevertheless, our work emphasizes the importance of sigma ligand-modulated intracellular calcium dynamics as a potential route of therapy, specifically for modulation of neuroinflammation.

BackgroundReliable population pharmacokinetic (PopPK) parameter estimation can be compromised by outliers under Gaussian residual error models. A common mitigation strategy is post hoc filtering based on conditional weighted residuals (CWRES); however, this approach can be insensitive due to model "masking" driven by variance inflation. Practical barriers to implementing robust likelihoods in standard software have motivated interest in computationally simpler exponential-tail alternatives such as the Laplace and exponential power distribution (EPD). MethodsWe implemented a one-compartment PopPK model using a custom likelihood workaround in Monolix to benchmark four residual error distributions: Normal, Laplace, Generalized Error Distribution (GED), and Students t. We assessed CWRES sensitivity under extreme contamination and compared estimation performance using theoretical tail-behavior analysis, controlled simulation studies spanning multiple contamination severities, and a real-world caffeine PK case study with influential terminal-phase deviations. ResultsSimulations showed that CWRES-based diagnostics can be unreliable: extreme outliers frequently produced |CWRES| < 6 because the Normal model inflated residual variance, thereby masking contamination. Exponential-tail models (Laplace, GED) improved robustness for mild to moderate outliers but failed under extreme deviations due to insufficiently heavy tails compared to power-law decay. In contrast, the Students t model, via power-law tail behavior, maintained stable and minimally biased structural parameter estimates across contamination scenarios. Consistent patterns were observed in the caffeine case study, where the Students t model provided improved fit and physiologically plausible parameter estimates. ConclusionsCWRES-driven outlier handling is methodologically fragile because influential contamination can be masked by variance inflation and induce biased inference. Among robust residual error models, exponential-tail distributions may be insufficient for extreme outliers, whereas the Students t distribution provides more stable inference across contamination severities. These findings support adopting Students t residual modeling as a default robust option in routine PopPK workflows when outlier contamination is plausible.

1Recent investigations assessed two non-linear mixed effect (NLME) model based approaches to test for drug effect on real data in the context of balanced two-arms designs. The standard approach (STD) showed type I error inflation and biased drug effect estimates contrary to the proposed alternative, individual model averaging (IMA), which had controlled type I error and unbiased drug effect estimates. The current study is an extension of the performances assessment of these two approaches to unbalanced designs and dose-response studies. The type I error rate and drug effect estimates were assessed for unbalanced designs, using placebo Alzheimer disease assessment scale cognitive (ADAS-cog) scores from 800 individuals. The bias in the drug effect estimates was assessed for dose response scenarios, on data modified by the addition of various dose-response scenarios (Emax= 2.5, 5, and 10). The generalization of IMA to any randomization ratio of two-arms studies was also presented, together with an alternative parameterization of IMA: saturated IMA (sIMA). Similarly to what was observed in balanced designs, both IMA and sIMA had controlled type I errors and unbiased drug effect estimates in unbalanced designs, whereas STD had uncontrolled type I error and biased drug estimates. For the dose-response studies STD had a systematic bias towards the underestimation of the drug effect estimates. IMA and sIMA were unbiased in the scenarios with high maximum effect but their performances were hindered at the lowest maximum drug effect scenario, because of the closeness in magnitude between the drug effect addition and the placebo model misspecification.