Frontiers in Pharmacology

IntroductionCannabinoid receptor-2 (CB2) is an emerging therapeutic target for chronic and inflammatory pain, cancer, and neurological disorders. Understanding the efficacy of CB2 ligands is crucial for future drug design and development. AimsWe aimed to establish a simple and robust system to control CB2 expression using a tetracycline-regulated mammalian expression system (T-REx), to enable application of the Black and Leff operational model to measure the operational efficacy ({tau}) of CB2 ligands. MethodsLigand-induced hyperpolarisation of AtT20 cells transfected with T-REx and human CB2 was measured by FLIPR membrane potential assay. Maximal and submaximal responses of the CB2 ligands were produced by regulating CB2 expression using tetracycline. Data were fitted to the operational model of receptor depletion to quantify the efficacy of seven ligands. Additionally, the maximal initial rate of signalling (IRmax), another putative measure of ligand efficacy, was determined. ResultsAK-F-064, CP55940 and 2-AG exhibited similar efficacy with a {tau} values of 11.4, 11 and 10.4 respectively, while anandamide (AEA) had the lowest efficacy ({tau}=1.07) among the tested agonists. The rank order of operational efficacy and IRmax was similar and was estimated as: AK-F-064 = CP55940 = 2-AG > 5F-AB-PICA = WIN55212-2 > HU-308 = AEA. ConclusionThis inducible expression system provides a reliable platform for quantifying and comparing CB2 ligand efficacy using the operational model. This approach may facilitate more precise CB2-targeted drug development and can be readily extended to other GPCR targets.

Pharmacological responses are modulated over time by regulation of signaling mechanisms. The canonical short-term regulation mechanisms are receptor desensitization and degradation of the response. Here for the first time a pharmacological model for measuring drug parameters is developed that incorporates short-term mechanisms of regulation of signaling. The model is formulated in a manner that enables measurement of drug parameters using familiar curve fitting methods. The efficacy parameter is k{tau}, which is simply the initial rate of signaling before it becomes limited by regulation mechanisms. The regulation parameters are rate constants, kDES for receptor desensitization and kD for response degradation. Efficacy and regulation are separate parameters, meaning these properties can be optimized independently of one another in drug discovery. The parameters can be applied to translate in vitro findings to in vivo efficacy in terms of the magnitude and duration of drug effect. When the time course data conform to certain shapes, for example the association exponential curve, a mechanism-agnostic approach can be applied to estimate agonist efficacy, without the need to know the underlying regulatory mechanisms. The model was verified by comparison with historical data and by fitting these data to estimate the model parameters. This new model for quantifying drug activity can be broadly applied to the short-term cell signaling assays used routinely in drug discovery and to aid their translation to in vivo efficacy, facilitating the development of new therapeutics.\n\nHighlightsO_LIRegulation of signaling impacts measurement of drug effect\nC_LIO_LIReceptor desensitization is incorporated here into a kinetic model of signaling\nC_LIO_LIDrug effect and signaling regulation can now be measured independently\nC_LIO_LIThe analysis framework is designed for signaling assays used in drug discovery\nC_LIO_LIThese new analysis capabilities will aid development of new therapeutics\nC_LI

Although being a relative term, agonist efficacy is a cornerstone in the proper assessment of agonist selectivity and signalling bias. The operational model of agonism (OMA) has become successful in the determination of agonist efficacies and ranking them. In 1983, Black and Leff introduced the slope factor to the OMA to make it more flexible and allow for fitting steep as well as flat concentration-response curves. Functional analysis of OMA demonstrates that the slope factor implemented by Black and Leff affects relationships among parameters of the OMA. Fitting of the OMA with Black & Leff slope factor to concentration-response curves theoretical model-based data resulted in wrong estimates of operational efficacy and affinity. In contrast, fitting the OMA modified by the Hill coefficient to the same data resulted in correct estimates of operational efficacy and affinity. Therefore, OMA modified by the Hill coefficient should be preferred over the Black & Leff equation for ranking of agonism and subsequent analysis, like quantification of signalling bias, when concentration-response curves differ in the slope factor and mechanism of action is known. Otherwise. Black & Leff equation should be used with extreme caution acknowledging potential pitfalls.

AO_SCPLOWBSTRACTC_SCPLOWThe human and canine renin-angiotensin-aldosterone-systems (RAAS) play a central role in the pathophysiology of congestive heart failure (CHF), justifying the use of angiotensin converting enzyme inhibitors inhibitors (ACEi) in this indication. Seminal studies in canine CHF had suggested that the pharmacological action of benazepril was relatively independent of doses > 0.25 mg/kg P.O, thereby providing a rationale for the European label dose of 0.25 mg/kg P.O q24h in dogs with cardiovascular diseases. However, most of these earlier studies on benazepril pharmacodynamics relied on measures of ACE activity - a sub-optimal endpoint to characterize the effect of benazepril on the RAAS. Nonlinear mixed-effects (NLME) modeling is an established framework for characterizing the effect of therapeutics on complex biological systems, such as the RAAS cascade. Importantly for therapeutic schedule optimization, one can use such a model to predict the outcomes of various hypothetical dosing schedules via simulation. The objectives of this study were (i) to expand on previous NLME modeling efforts of the dose-exposure-response relationship of benazepril on biomarkers of the RAAS which are relevant to CHF pathophysiology and disease prognosis {angiotensins I, II, III, IV, (1-7)} by using a quantitative systems pharmacology (QSP) modeling approach; and (ii) to develop a software implementation of the model capable of simulating clinical trials in benazepril in dogs bedside dose optimization. This study expands on previous modeling efforts to characterize the changes in RAAS pharmacodynamics in response to benazepril administration and showcase how QSP modeling can be used for efficient dose optimization of ACEis at the bedside. Our results suggest that 0.5 mg/kg PO q12h of benazepril produced the most robust reduction in AngII and upregulation of RAAS alternative pathway biomarkers. This model will eventually be expanded to include relevant clinical endpoints, which will be evaluated in an upcoming prospective trial in canine patients with CHF. AO_SCPLOWUTHORC_SCPLOW SO_SCPLOWUMMARYC_SCPLOWCongestive heart failure (CHF) is a disease of the heart, common to both dogs and humans, where the heart is not healthy enough to pump blood around the body efficiently. Because the blood isnt moving around the body as efficiently, it tends to get congested in various areas of the body and increases strain on the heart. Benazepril is a drug for CHF used in both dogs and humans to reduce congestion and improve the functioning of the cardiovascular system. Although benazepril is effective, theres evidence that suggests the dosing could be improved if the therapeutic was further studied. In this experiment, we tested benazepril at several safe dosages in well-cared for and healthy dogs to collect data on the relationship between dose size, dosing frequency, and effect on the cardiovascular system. Using this data, we built computer models of benazepril to simulate many clinical trials. By studying these simulations, we were able to make several predictions about the optimal dosing schedule of benazepril in dogs. Weve also built a web-app version of the computer model for veterinary researchers to use, modify, and study. This work also provides a platform and roadmap for optimizing benazepril dosages in human CHF.

We have developed an automated patch-clamp protocol that allows high information content screening of sodium channel inhibitor compounds. We have observed that individual compounds had their specific signature patterns of inhibition, which were manifested irrespective of the concentration. Our aim in this study was to quantify these properties. Primary biophysical data, such as onset rate, the shift of the half inactivation voltage, or the delay of recovery from inactivation, are concentration-dependent. We wanted to derive compound-specific properties, therefore, we had to neutralize the effect of concentration. This study describes how this is done, and shows how compound-specific properties reflect the mechanism of action, including binding dynamics, cooperativity, and interaction with the membrane phase. We illustrate the method using four well-known sodium channel inhibitor compounds, riluzole, lidocaine, benzocaine, and bupivacaine. Compound-specific biophysical properties may also serve as a basis for deriving parameters for kinetic modeling of drug action. We discuss how knowledge about the mechanism of action may help to predict the frequency-dependence of individual compounds, as well as their potential persistent current component selectivity. The analysis method described in this study, together with the experimental protocol described in the accompanying paper, allows screening for inhibitor compounds with specific kinetic properties, or with specific mechanisms of inhibition.

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

Population pharmacokinetic (PK) model-based Bayesian estimation is widely used for dose individualization, particularly when sample availability is limited. However, its predictive accuracy can be compromised by factors such as misspecified prior information, intra-patient variability, and uncertainties in PK variations. In this study, we developed a hybrid approach that combines machine learning (ML) with population PK-based Bayesian methods to improve the prediction of infliximab concentrations in children with Crohns disease. We calculated prediction errors between Bayesian-estimated and observed infliximab concentrations from 292 measurements across 93 patients. Incorporating clinical patient features, we explored various ML algorithms, including linear regression, random forest, support vector regression, neural networks, and XGBoost to correct the Bayesian-based prediction errors. The predictive performance of these ML models was assessed using root mean square error (RMSE) and mean prediction error (MPE) with 5-fold cross-validation. For Bayesian estimation alone, the RMSE and MPE were 4.8 {micro}g/mL and -0.67 {micro}g/mL, respectively. Among the ML algorithms, the XGBoost model demonstrated the best performance, achieving an RMSE of 3.78 {+/-} 0.85 {micro}g/mL and an MPE of -0.03 {+/-} 0.69 {micro}g/mL in 5-fold cross-validation. The ML-corrected Bayesian estimation significantly reduced the absolute prediction error compared to Bayesian estimation alone. This hybrid population PK-ML approach provides a promising framework for improving the predictive performance of Bayesian estimation, with the potential for continuous learning from new clinical data to enhance dose individualization. Key pointsO_LIA new hybrid model combining population pharmacokinetic model-based Bayesian estimation and machine learning significantly improved the accuracy of infliximab concentration predictions in young adult and pediatric patients with Crohns disease. C_LIO_LIThe developed hybrid model can facilitate infliximab individualized dosing by accounting for changes in clinical conditions and patient-specific factors that the conventional Bayesian estimation approach may not address, and can be integrated into precision dosing dashboards, such as RoadMAB, for real-world clinical application. C_LIO_LIThis study indicates that model predictive accuracy can be enhanced by combining the Bayesian method with machine learning, even with a relatively small amount of clinical data. This is particularly encouraging for specific populations, such as pediatric patients, where obtaining rich clinical data is challenging. C_LI

This study focuses on RU-1205, a new kappa-opioid agonist exhibiting analgesic effect without causing dysphoric or aversive reactions. It is assumed that the absence of dysphoric or aversive effects can be attributed to functional selectivity or it might be due to an additional mechanism of action that involves blocking the p38 mitogen-activated protein kinase (MAPK). The aimof this study was the experimental identification of the mechanisms of action of RU-1205 associated with inhibition of MAPK p38 and functional selectivity at kappa-opioid receptors. Materials and methodsRats weighing 260-280 g were implanted with chronic cortical and deep electrodes. LFP activity was recorded after intracerebroventricular administration of well-studied reference substances: the selective kappa-opioid agonist U-50488 at a dose of 100 g; the MAPK p38 blocker SB203580 at a dose of 1 g; and the investigational compound RU-1205 at 350 g. The weighted phase lag index (WPLI) was calculated. Subsequently, machine learning techniques were employed to reduce dimensionality and extract connectivity features using the principal component analysis method. Finally, signal classification was conducted using models based on Gaussian processes. By applying the patch-clamp technique in the whole-cell configuration, the spike activity of pyramidal neurons in the basolateral amygdala was studied. The neurons were identified by their accommodation properties. After local perfusion of the test compounds, 3 dose-response curves were obtained for: (1) U-50488 at concentrations ranging from 0.001 to 10 M; (2) combinations of U-50488 (0.001-10 M) and RU-1205 (10 M); and (3) combinations of U-50488 (0.01-10 M) and RU-1205 (100 M). ResultsThe developed models were able to classify the compound RU-1205 as a <<non-inhibitor>> of MAPK p38 with a probability of 0.89. The results obtained were confirmed in patch clamp experiments on acute brain slices, where it was demonstrated that U-50488 statistically significantly increases the spike activity of pyramidal neurons in the basolateral amygdala (p <0.05) and RU-1205 interacts with U-50488, suppressing its effect on the spike activity of neurons. ConclusionsThe findings suggest that compound RU-1205 displays properties consistent with a functional kappa opioid receptor agonist and does not have a significant effect on MAPK p38. The study demonstrates the possibility of integrating electrophysiological measurements and advanced data analysis methods for a deep understanding of neuronal mechanisms of drug action and underscores the potential for further research in this area.

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSAllometry is traditionally used in drug development in order to extrapolate and predict pharmacokinetic (PK) parameter estimates, such as steady-state volume of distribution (Vss), clearance (CL), oral clearance (CL/F, where F is the oral bioavailability) in animal species to humans. Recent results show that in silico prediction methodology can improve and outperform laboratory data-based methods for predictions of Vss and F. The main objectives were to evaluate the simple allometry principle for CL and CL/F, how well simple allometry predicts CL and CL/F in humans, and whether a combination of simple allometry and in silico predictions can improve predictions of CL and CL/F in humans. MethodsThe literature was searched for CL and CL/F-data in animal species and man. Data from at least 2 species, and preferably 3 or 4, and humans, for each compound (only small drugs) were used for the evaluation. The software ANDROMEDA by Prosilico was used for in silico predictions. A rule-based approach, based on laboratory and in silico data, and 2D PK-Space, were used to localize and predict compounds with largest allometric errors. Results and DiscussionThe evaluation shows limited support for the theoretical basis and empirical evidence and applicability of simple allometry for the prediction of CL and CL/F. There are many deviations from the simple allometric relationship (such as relatively low liver weight and blood flow in humans and 140-fold underprediction to 5,800-fold overprediction), skewness (including general overprediction, cases where humans deviate, and relatively high CL and low F in rats) and limited interspecies relationships (R2=0.07-0.19 for CL in animals vs humans). With 43 qualified compounds, R2 for CL and CL/F with simple allometry reached ca 1/3 (log scales). With the combination of simple allometry and in silico it was possible to improve the predictive performance. 31 to 124 % (average 68 %) improvement were found for R2, <2-fold error and median error, whereas maximum errors were reduced to 1/13 to 1/6. In silico predictions alone were even better - 19 to 176 % (average 96 %) and 1/157 to 1/25, respectively. 3 and 4 different PK-rules for large allometric prediction errors were found for CL and CL/F, respectively. These also had distinct positioning in 2D PK-Space. With in silico predictions, errors for the compounds with largest allometric prediction errors were decreased by 7-fold for CL and 4-fold for CL/F (median). ConclusionSimple allometry is associated with limited theoretical and empirical support for predictions of CL and CL/F in humans, and can be clearly improved when combined with (or replaced by) new in silico prediction methodology, rules and 2D PK-Space positioning. This is in line with the ambition to reduce and replace animal testing in drug development and need for methodological improvement.

IntroductionInfection and inflammation elevate circulating pro-inflammatory cytokines that can affect renal drug clearance. Accordingly, we sought to (i) quantify the extent of modulation of renal drug-metabolizing enzymes and transporters (DMETs) by cytokines and (ii) identify the mechanism(s) underlying these effects. MethodsFresh primary human proximal tubular epithelial cells (PTECs) were cultured on extracellular matrix-coated Transwells. PTECs were exposed every 24 h, for 48 h, to IL-6, IL-1{beta}, TNF-, IFN-{gamma}, IL-4, or IL-10 (0.1 or 1 ng/mL), individually or as a cocktail. mRNA expression of 25 renal DMETs was quantified by RT-qPCR. Individual activity of OAT1-4, OCT2, and OCTN1 was measured. To determine mechanisms of these effects, selective MAPK/NF-{kappa}B inhibitors (ERK [PD98059], p38MAPK [SB203580], JNK [SP600125], and NF-{kappa}B [PDTC]), individually or as a cocktail, were used. IL-6, soluble IL-6 receptor (sIL-6R), and IL-6 + sIL-6R were used to probe endogenous/exogenous IL-6 classic versus trans-signaling. ResultsIL-1{beta} was the predominant modulator, downregulating mRNA expression of OAT1-3, OCT2, OAT4, MATE2-K, MRP2, and OATP4C1, and upregulating mRNA expression of OCTN1 and MRP3. TNF- downregulated OAT1-3 mRNA expression to an extent similar to IL-1{beta}, but did not affect other transporters. Activity changes for the major uptake transporters mirrored mRNA directionality. MAPK/NF-{kappa}B blockade by the inhibitor cocktail reduced IL-6 secretion while completely reversing the IL-1{beta}-driven downregulation of OAT1-3 mRNA. JNK inhibition alone restored OAT1/3 mRNA. Inhibition of p38MAPK blunted OAT2 mRNA downregulation. OCTN1 mRNA induction required NF-{kappa}B. Downregulation of OAT4/OCT2 mRNA was largely MAPK/NF-{kappa}B-independent. IL-6 alone, sIL-6R alone, or IL-6 + sIL-6R did not reproduce IL-1{beta}-driven changes in transporter mRNA. ConclusionsIL-1{beta} is the principal driver of cytokine-mediated regulation of human renal transporters in PTECs via JNK/p38MAPK/NF-{kappa}B nodes. These mechanistic, exposure-verified data provide inputs for physiologically based pharmacokinetic predictions of renal secretory clearance and pathway-mediated drug interactions during inflammation. Visual Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/690608v1_ufig1.gif" ALT="Figure 1"> View larger version (64K): org.highwire.dtl.DTLVardef@177e372org.highwire.dtl.DTLVardef@1f56bc4org.highwire.dtl.DTLVardef@17612e3org.highwire.dtl.DTLVardef@d2291f_HPS_FORMAT_FIGEXP M_FIG C_FIG Translational StatementSystemic inflammation increases cytokine concentrations and alters drug pharmacokinetics. Yet, cytokine regulation of renal drug transporters remains poorly defined, even though the kidney clears many anti-infective drugs via active secretion. Using an optimized primary human proximal tubular epithelial cell model that preserves expression and function of major renal transporters, we found that IL-1{beta} is the predominant cytokine that downregulates the mRNA and activity of OAT1-3, OCT2, and OAT4, while upregulating the mRNA and activity of OCTN1. We further showed that IL-1{beta}-driven downregulation of OAT1/3 occurs through JNK signaling, OAT2 through p38MAPK, and OCTN1 through NF-{kappa}B. These data provide quantitative inputs for physiologically based pharmacokinetic models to predict how inflammation alters renal transporter-mediated drug clearance, informing dose adjustment and risk assessment for disease-drug and drug-drug interactions in patients with inflammatory kidney disease or systemic infections. They also highlight signaling nodes where anti-inflammatory therapies might inadvertently modify renal drug transport.

Drug repurposing may provide a solution to the substantial challenges facing de novo drug development. Given that 66% of FDA-approved drugs in 2021 were supported by human genetic evidence, drug repurposing methods based on genome wide association studies (GWAS), such as drug gene-set analysis, may prove an efficient way to identify new treatments. However, to our knowledge, drug gene-set analysis has not been tested in non-psychiatric phenotypes, and previous implementations may have contained statistical biases when testing groups of drugs. Here, 1201 drugs were tested for association with hypercholesterolemia, type 2 diabetes, coronary artery disease, asthma, schizophrenia, bipolar disorder, Alzheimers disease, and Parkinsons disease. We show that drug gene-set analysis can identify clinically relevant drugs (e.g., simvastatin for hypercholesterolemia [p = 2.82E-06]; mitiglinide for type 2 diabetes [p = 2.66E-07]) and drug groups (e.g., C10A for coronary artery disease [p = 2.31E-05]; insulin secretagogues for type 2 diabetes [p = 1.09E-11]) for non-psychiatric phenotypes. Additionally, we demonstrate that when the overlap of genes between drug-gene sets is considered we find no groups containing approved drugs for the psychiatric phenotypes tested. However, several drug groups were identified for psychiatric phenotypes that may contain possible repurposing candidates, such as ATC codes J02A (p = 2.99E-09) and N07B (p = 0.0001) for schizophrenia. Our results demonstrate that clinically relevant drugs and groups of drugs can be identified using drug gene-set analysis for a number of phenotypes. These findings have implications for quickly identifying novel treatments based on the genetic mechanisms underlying diseases.

Novel synthetic opioids (NSOs), including both fentanyl and non-fentanyl analogs that act as the -opioid receptor (MOR) agonists, are associated with serious intoxication and fatal overdose. Previous studies proposed that G protein biased MOR agonists are safer pain medications, while other evidence indicates that low intrinsic efficacy at MOR better explains reduced opioid side effects. Here, we characterized the in vitro functional profiles of various NSOs at MOR using adenylate cyclase inhibition and {beta}-arrestin2 recruitment assays, in conjunction with the application of the receptor depletion approach. By fitting the concentration-response data to the operational model of agonism, we deduced the intrinsic efficacy and affinity for each opioid in the Gi protein signaling and {beta}-arrestin2 recruitment pathways. Compared to the reference agonist DAMGO, we found that several fentanyl analogs were more efficacious at inhibiting cAMP production, whereas all fentanyl analogs were less efficacious at recruiting {beta}-arrestin2. In contrast, the non-fentanyl 2-benzylbenzimidazole (i.e., nitazene) analogs were highly efficacious and potent in both the cAMP and {beta}-arrestin2 assays. Our findings suggest that the high intrinsic efficacy of the NSOs in Gi protein signaling is a common property that may underlie their high risk of intoxication and overdose, highlighting the limitation of using in vitro functional bias to predict the adverse effects of opioids. Instead, our results show that, regardless of bias, opioids with sufficiently high intrinsic efficacy can be lethal, especially given the extremely high potency of many of these compounds that are now pervading the illicit drug market.

Covariate selection in population pharmacokinetics modelling is essential for understanding interindividual variability in drug response and optimizing dosing. Traditional stepwise covariate modelling is often time-consuming, compared to the new machine learning alternatives. This study investigates the use of Neural Networks with Stochastic Gates for automated covariate selection, aiming to efficiently identify relevant covariates while penalizing excessive covariate inclusion. On various synthetic datasets the approach demonstrated robustness in detecting important covariates, overcoming challenges such as high correlations, low covariate frequencies, high interindividual variability and complex covariate dependencies. In real clinical data from a monalizumab study, the method successfully identified covariates that matched those found by experts. However, for tixagevimab/cilgavimab, it identified a superset of covariates, indicating a potential need for further pruning. This machine learning-based method enhances the covariate pre-selection process in population pharmacokinetics model development, offering significant time savings and improving efficiency even under challenging scenarios. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=89 SRC="FIGDIR/small/656586v1_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@1f5180borg.highwire.dtl.DTLVardef@1fd97e7org.highwire.dtl.DTLVardef@1ffc3d0org.highwire.dtl.DTLVardef@90c207_HPS_FORMAT_FIGEXP M_FIG C_FIG Study HighlightsO_ST_ABSWHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC?C_ST_ABSCovariate selection in population pharmacokinetics modelling is a crucial step in drug development and dosing determination. Traditionally, this process is conducted in a stepwise manner, which can be very time-consuming. Recently, fast machine learning (ML)-based methods for covariate selection have emerged in the literature, offering more efficient alternatives. WHAT QUESTION DID THIS STUDY ADDRESS?Can we use Neural Networks with Stochastic Gates, incorporating explicit penalization on the number of covariates, to select superior set of covariates compared to prior ML-based methods? WHAT DOES THIS STUDY ADD TO OUR KNOWLEDGE?Neural Networks with Stochastic Gates provide a reliable approach in accurately detecting covariates because they can effectively eliminate irrelevant covariates even in cases with high inter-covariates correlation. HOW MIGHT THIS CHANGE DRUG DISCOVERY, DEVELOPMENT, AND/OR THERAPEUTICS?This approach accelerates pharmacokinetic model development by significantly reducing the time required for covariate evaluation. Additionally, it allows for the screening of a wider set of covariates, potentially leading to fewer falsely missed covariates and better-quality models, a task that would be infeasible with traditional stepwise covariate modelling.

AimsTo determine the suspected adverse drug reaction (ADR) profile of leukotriene receptor antagonists (LTRAs: montelukast and zafirlukast) relative to first-line asthma medications short-acting beta agonists (SABA: salbutamol) and inhaled corticosteroid (ICH: beclomethasone) in the United Kingdom. To determine chemical and pharmacological rationale for the suspected ADR signals. MethodsProperties of the asthma medications (pharmacokinetics and pharmacology) were datamined from the chemical database of bioactive molecules with drug-like properties, European molecular Biology laboratory (ChEMBL). Suspected ADR profiles of the asthma medications was curated from the Medicines and Healthcare products Regulatory Authority (MHRA) Yellow Card interactive drug analysis profiles (iDAP) and concatenated to the standardised prescribing levels (Open Prescribing) between 2018-2023. ResultsTotal ADRs per 100,000 Rx (P < .001) and psychiatric system organ class (SOC) ADRs (P < .001) reached statistical significance. Montelukast exhibited the greatest ADR rate at 15.64 per 100,000 Rx. The low lipophilic ligand efficiency (LLE = 0.15) of montelukast relative to the controls may explain the promiscuity of interactions with off-target G-coupled protein receptors (GPCRs). This included the dopamine signalling axis, which in combination with bioaccumulation in the cerebrospinal fluid (CSF) to achieve Cmax beyond a typical dose can be ascribed to the psychiatric side effects observed. Cardiac ADRs did not reach statistical significance but inhibitory interaction of montelukast with the MAP kinase p38 alpha (a cardiac protective pathway) was identified as a potential rationale for montelukast withdrawal cardiac effects. ConclusionRelative to the controls, montelukast displays a range of suspected system organ class level ADRs. For psychiatric ADR, montelukast is statistically significant (P < .001). A mechanistic hypothesis is proposed based on polypharmacological interactions in combination with CSF levels attained. This work further supports the close monitoring of montelukast for neuropsychiatric side effects.

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.

Background and PurposeMetabotropic glutamate receptors (mGlus) are obligate dimer G protein coupled receptors that can all homodimerize and heterodimerize in select combinations. Responses of mGlu heterodimers to selective ligands, including orthosteric agonists and allosteric modulators, are largely unknown. Experimental ApproachThe pharmacological properties of each group II and III mGlu homodimer (except mGlu6) and several heterodimers were examined when stochastically assembled in HEK293T cells, or specifically measured using an improved G protein mediated BRET assay employing complimented fragments of NanoLuciferase. ResultsStochastically assembled receptors adopted unique signaling characteristics. Some favored the potency, efficacy or signaling kinetics of a dominant subunit, while others exhibited blended profiles reflective of a combination of homo- and heterodimers at various ratios of expressed receptor. Finally, group II and III mGlu dimers were examined for responses to selective agonists and allosteric modulators. Effects of glutamate and selective group II and III orthosteric agonists were found to result in unique concentration response profiles when examining each combination of group II and II mGlu. Effects of select allosteric modulators were examined for each mGlu2 containing dimer as well as several group III dimer pairs. Likewise, allosteric modulator effects were often unique across dimers containing the targeted subunit of the ligand being tested. ConclusionsResults demonstrate that mGlu dimers respond uniquely to selective ligands, and show that the mGlu family is not governed by generalizable rules dictating consequences of dimeric subunit interactions leading to signaling consequences.

Sigmoid curve (S-curve) is a basic exhibition form of dose-effect relationship in drug reaction. To analyze S-curve is an important method to well-understand drug reaction performance (DRP). The present study introduced an S-curve analysis method for pharmacological experiment results (PERs), the core of which was to solve the problem of the linear fitting of S-curve equation (S-Eq). The linear fitting Eqs of S-Eq were established with 100% fitness. Meanwhile, mathematical and pharmacological meaning of S-curve constants, ED50 and maximum effect (ymax) were clarified. The same group of experimental data was analyzed by the present method and four traditional analysis methods. The result indicates that the experimental parameters and their values displaying DRP got by different methods are different. The S-curve analysis method is closer to real drug reaction law.

IntroductionCoronavirus disease 2019 (COVID-19) can be associated with mortality and high morbidity worldwide. There is an extensive effort to control infection and disease caused by SARS-CoV-2. This study addressed the hypothesis that angiotensin II type I receptor blocker, Losartan, may restrict pathogenesis caused by SARS-CoV-2 by decreasing viral-induced cytopathological changes by blocking angiotensin II type 1 receptor (AT1R), thus reducing the affinity of the virus for ACE2, and inhibiting papain-like protease of the virus. MethodLosartan inhibitory effect on deubiquitination and deISGylation properties of papain-like protease was investigated using a fluorescence method and gel shift analysis determining its inhibitory effects. The inhibitory effect of Losartan on SARS-CoV-2 cell replication was investigated both when losartan was added to the cell culture 1 hour before (pre-infection group) and 1 hour after (post-infection group) SARS-CoV-2 infection of Vero E6 cells. ResultsLosartan treatment of Vero E6 cells prior to and after SARS-CoV-2 infection reduced SARS-CoV-2 replication by 80% and 70% respectively. Losartan was not a strong deubiquitinase and deISGylase inhibitor of PLpro. ConclusionLosartan added pre- and post-infection to the Vero E6 cell culture significantly prevents cell destruction and replication by SARS-CoV2. Losartan has low side-effects, is readily available, and can be produced at high levels globally, all features of a promising drug in treatment of COVID-19 if validated by clinical trials.

Background and purposeMuscarinic acetylcholine receptors are key therapeutic targets, and ligands engaging both orthosteric and allosteric sites may offer improved selectivity and efficacy. The muscarinic antagonist KH-5 displays functional antagonistic potency exceeding its binding affinity, suggesting a non-classical mechanism of action. Here, we investigated whether KH-5 acts as a dualsteric antagonist and defined its mode of interaction with muscarinic receptors. Experimental approachFunctional responses at human M1 and M2 receptors expressed in CHO cells were assessed using inositol phosphate accumulation and [35S]GTP{gamma}S binding, respectively. Radioligand binding studies employed orthosteric antagonists and agonists in combination with KH-5 and classical allosteric modulators. Data were analysed using competitive, allosteric, and dualsteric binding and operational models. Molecular docking, molecular dynamics simulations, and site-directed mutagenesis were used to identify structural determinants of KH-5 binding. Key resultsKH-5 antagonised responses to multiple agonists in a saturable and probe-dependent manner consistent with an allosteric interaction. However, KH-5 did not decrease maximal response to agonists, contradicting simple allosteric antagonism. At M2 receptors, antagonism was largely competitive. Binding studies revealed transient enhancement of agonist binding at M1 receptors at nanomolar concentrations of KH-5, best described by a dualsteric binding model involving independent orthosteric and ectopic site interactions. KH-5 did not bind to the classical muscarinic allosteric site at the second extracellular loop but interacted with an extracellular vestibule site, supported by molecular modelling and mutation of key residues. Conclusions and implicationsThe simplest model explaining the KH-5 mechanism of action at muscarinic receptors combines two concurrent modes of interaction. From the allosteric site, it positively modulates functional responses to agonists. From the orthosteric site, it exerts competitive antagonism of functional responses. Additionally, molecules of KH-5 bound to allosteric and orthosteric sites exert positive cooperativity. HighlightsO_LIKH-5 antagonises muscarinic receptors with a potency exceeding its orthosteric binding affinity C_LIO_LIFunctional antagonism shows probe dependence, indicating an allosteric component C_LIO_LIBinding studies support independent interaction of KH-5 with orthosteric and ectopic sites C_LIO_LIKH-5 does not bind the classical muscarinic allosteric site C_LIO_LIExcept for xanomeline, the operational model of dualsterically modulated agonism explains the complex pharmacology of KH-5 at M1 receptors C_LI

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.