Drug Effects In Models Research Articles

Concentration-QTcF analysis of quizartinib in patients with newly diagnosed FLT3-internal-tandem-duplication-positive acute myeloid leukemia.

Quizartinib prolongs QT interval through inhibition of the slow delayed rectifier potassium current (IKs). We used non-linear mixed-effects modeling to explore the relationship between quizartinib and its pharmacologically active metabolite AC886 and the Fridericia-corrected QT interval (QTcF) in newly diagnosed acute myeloid leukemia (AML) patients. We evaluated linear and non-linear drug effect models, using triplicate QTcF measurements with available time-matched pharmacokinetic samples from the Phase 3 QuANTUM-First trial. The effect of intrinsic and extrinsic factors on model parameters was tested using stepwise covariate model building. Simulations were conducted to predict the change from baseline in QTcF (ΔQTcF) at the maximum concentration at steady-state (Cmax,ss) for quizartinib maintenance daily doses of 30 and 60 mg. The concentration-QTcF (C-QTcF) relationship was best described by a sigmoidal maximum effect model. After accounting for the effect of quizartinib, including AC886 concentrations did not further explain changes in QTcF. Circadian variations in QTcF were described using an empirical change from baseline based on clock times. Age and hypokalaemia were identified as statistically significant covariates on baseline QTcF; no covariates were found to impact the C-QTcF relationship. The median model-predicted ΔQTcF at Cmax,ss was 18.4 ms (90% confidence interval (CI): 16.3-20.5) at 30 mg and 24.1 ms (90% CI: 21.4-26.6) at 60 mg. In conclusion, in newly diagnosed AML patients, ΔQTcF increased non-linearly with increasing quizartinib concentrations. The predicted ΔQTcF increase at Cmax,ss supports the proposed dose adaptation based on observed QTcF and the dose reduction in case of strong cytochrome P450 3A (CYP3A) inhibitors coadministration.

Exposure-Response Analysis of Tofacitinib in Active Psoriatic Arthritis: Results from Two Phase 3 Studies.

Tofacitinib is an oral Janus kinase inhibitor for the treatment of psoriatic arthritis (PsA). These post hoc exposure-response (E-R) analyses of pooled data from two Phase 3 studies (NCT01877668 and NCT01882439) characterized the relationships between tofacitinib exposure and efficacy (American College of Rheumatology [ACR] criteria), and changes in hemoglobin (Hgb) in patients with PsA. Efficacy data for the proportion of patients receiving tofacitinib 5 or 10 mg twice daily, or placebo, achieving ACR ≥20%, ≥50%, or ≥70% response criteria (ACR20, ACR50, and ACR70, respectively) at Month 3, were modeled jointly using a four-category ordered categorical exposure-response model (ACR20 non-responder, ACR20 responder but not ACR50 responder, ACR50 responder but not ACR70 responder, and ACR70 responder). A maximum drug effect (Emax) model (using average concentrations of tofacitinib at steady state [Cavg]) adequately described the exposure-ACR response rate relationship. Model-predicted response rates for tofacitinib 5 and 10 mg twice daily were 51% and 58%, respectively, for ACR20; 29% and 36% for ACR50; and 15% and 20% for ACR70. The E-R relationship between tofacitinib exposure and changes in Hgb was assessed using an indirect response model, which generally predicted Hgb concentration-time profiles across treatments well. The proportions of patients experiencing a decrease in Hgb of >2 g/dL were similar with tofacitinib 5 mg twice daily or placebo. These results were generally consistent with previous analyses in rheumatoid arthritis and psoriasis, and support the use of tofacitinib 5 mg twice daily for active PsA.

Modeling the Impact of Ensitrelvir on SARS-CoV-2 Dynamics and Its Application for Assessment of Transmission Mitigation of Patients with COVID-19.

Mathematical modeling can provide quantitative understanding of the viral dynamics and viral reduction effects of drugs and enable simulations of the dynamics in various scenarios. In this study, a drug effect model of ensitrelvir was developed to describe the viral reduction effect. Using the model, we also estimated the impact of treatment with ensitrelvir on the reduction in the number of infected patients at the population level in Japan. The drug effect model of ensitrelvir was developed based on a viral dynamic model for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and a population pharmacokinetic model of ensitrelvir using 10,477 samples of viral load from 1447 patients with coronavirus disease 2019 (COVID-19) in a phase 2/3 study. It was assumed that the drug effect on SARS-CoV-2 promoted the viral clearance depending on the free plasma concentrations. We estimated the impact of ensitrelvir treatment on the reduction in the number of infected patients at the population level in Japan using the susceptible-infectious-recovered-susceptible (SIRS) model including transmission mitigation. The viral reduction effect of ensitrelvir was characterized as a promotion of viral clearance depending on the plasma ensitrelvir concentrations using the Emax model. The maximum reduction effect was considered to depend on the time from symptom onset to treatment. The maximum transmission mitigation effect was observed when treatment was initiated within 12-24h of symptom onset, and secondary infections could be reduced by administering ensitrelvir as soon as possible after symptom onset. The viral reduction by ensitrelvir could be characterized based on the viral dynamics, and the dynamics could be estimated using the drug effect model. Furthermore, the drug effect on population level transmission based on the dynamics could be estimated. Thus, the simulation could be conducted for various conditions.

Model-Informed Precision Dosing for Personalized Ustekinumab Treatment in Plaque Psoriasis.

Implementing model-informed precision dosing (MIPD) strategies guided by population pharmacokinetic/pharmacodynamic (PK/PD) models could enhance the management of inflammatory diseases such as psoriasis. However, the extent of individual experimental data gathered during MIPD significantly influences the uncertainty in estimating individual PK/PD parameters, affecting clinical dose selection decisions. This study proposes a methodology to individualize ustekinumab (UTK) dosing strategies for 23 Spanish patients with moderate to severe chronic plaque psoriasis., considering the uncertainty of individual parameters within a population PK/PD model. An indirect response model from previous research was used to describe the PK/PD relationship between UTK serum concentrations and the Psoriasis Area and Severity Index (PASI) score. A maximum inhibition drug effect (Imax) model was selected, and a first-order remission constant rate of psoriatic skin lesion (kout = 0.016 d-1) was estimated. The MIPD approach predicted that 35% and 26% of the patients would need an optimized and intensified dosage regimen, respectively, compared to the regimen typically used in clinical practice. This analysis demonstrated its utility as a tool for selecting personalized UTK dosing regimens in clinical practice in order to optimize the probability of achieving targeted clinical outcomes in patients with psoriasis.

Model-based meta-analysis to quantify the effects of short interfering RNA therapeutics on hepatitis B surface antigen turnover in hepatitis B-infected mice.

The objective of this study was to compare the efficacy of short interfering RNA therapeutics (siRNAs) inreducing hepatitis B surface antigen (HBsAg) levels in hepatitis B-infected (HBV) mice across multiple siRNA therapeutic classesusing model-based meta-analysis (MBMA) techniques. Literature data from 10 studies in HBV-infected mice were pooled, including 13 siRNAs, formulated as liposomal nanoparticles (LNPs) or conjugated to either cholesterol (chol) or N-acetylgalactosamine (GalNAc). Time course of the baseline- and placebo-corrected mean HBsAg profiles were modeled using kinetics of drug effect (KPD) model coupled to an indirect response model (IRM) within a longitudinal non-linear mixed-effects MBMA framework. Single and multiple dose simulations were performed exploring the role of dosing regimens across evaluated siRNA classes. The HBsAg degradation rate (0.72 day-1) was consistent across siRNAs but exhibited a large between-study variability of 31.4% (CV%). The siRNA biophase half-life was dependent on the siRNA class and was highest for GalNAc-siRNAs (21.06 days) and lowest for chol-siRNAs (2.89 days). ID50 estimates were compound-specific and were lowest for chol-siRNAs and highest for GalNAc-siRNAs. Multiple dose simulations suggest GalNAc-siRNAs may require between 4 and 7 times less frequent dosing at higher absolute dose levels compared to LNP-siRNAs and chol-siRNAs, respectively, to reach equipotent HBsAg-lowering effects in HBV mice. In conclusion, non-clinical HBsAg concentration-time data after siRNA administration can be described using the presented KPD-IRM MBMA framework. This framework allows to quantitatively compare the effects of siRNAs on the HBsAg time course and inform dose and regimen selection across siRNA classes. These results may support siRNA development, optimize preclinical study designs, and inform data analysis methodology of future anti-HBV siRNAs; and ultimately, support siRNA model-informed drug development (MIDD) strategies.

Pharmaco-Analysis of Psychedelics—Philo-Fictions about New Materialism, Quantum Mechanics, Information Science, and the Philosophy of Immanence

Recent developments regarding the pharmacology of psychoactive substances are significant for treating depressions or opioid addictions. Current theories, hypotheses, and models of drug effects assume a cause–effect narrative, which is based on a stimulus/response mechanism. These narratives prioritize effects rather than conscious experiences. In this sense, drug experiences are quickly subsumed into common categories and codes of biological determinism. If subjective experiences are in the focus of the research, it quickly becomes a link to mystical, spiritual, or transcendental narratives. These classifications lead to epistemological doublets (Gadamer). In this article, psychedelic experiences of drug users are analyzed in the frame of the pharmaco-analysis by Deleuze/Guattari. These framed psychedelic experiences are interpreted by means of a non-philosophical approach through philo-fictions (Laruelle), i.e., contradictory assumptions and hyperspeculations. In this respect, the aim of this article is to bring philo-fictions in relation to psychedelic experiences and to discuss them with models of information science, quantum mechanics, new materialism, and the philosophy of immanence. The result will be an open synthesis, with the assumption of further reflections on the agency, immanence, and the wholeness of matter.

Modeling Approach with Therapeutic Drug Monitoring Data to Describe Time Course of Clozapine Exposure and Positive and Negative Syndrome Scale Scores

The Positive and Negative Syndrome Scale (PANSS) is commonly used to assess the severity of the clinical symptoms of schizophrenia (SCZ). This study aimed to develop a pharmacokinetic (PK)/pharmacodynamic (PD) model based on therapeutic drug monitoring (TDM) data to characterize the relationship between clozapine exposure and the PANSS scores in patients with SCZ. TDM data for clozapine and PANSS scores from 45 patients with SCZ were included in this modeling analysis using NONMEM. Based on published data, intensive PK sampling data collected up to 12 hours postdose from 23 patients was incorporated into the PK data set to improve the fitting of absorption and disposition. For PD model development, the PANSS score was assessed at baseline, followed by 8 and 18 weeks after the initiation of clozapine dosing. Visual predictive check plots, the precision of parameter estimates, and decreases in the minimum objective function values were used for the model evaluation. A 2-compartment model with an absorption lag and a combined error model adequately described the PK of clozapine. The implementation of disease progression with placebo and drug effects improved the model's ability to describe the time course of the PANSS scores. In the final PK/PD model, Weibull and maximum effect (E max ) models were selected as disease progression models for the placebo and drug effect models, respectively. The model evaluation results supported the adequacy of the final model. A clozapine PK/PD model based on clinical settings adequately described the PANSS time course in patients with SCZ. These findings may aid the development of treatment strategies for patients with SCZ.

PK/PD modeling to characterize placebo and treatment effect of omalizumab for chronic spontaneous urticaria.

The pharmacokinetic (PK) characteristics of omalizumab and its pharmacodynamic (PD) effect in patients has yet to be fully characterized in chronic spontaneous urticaria, which could elucidate its pathogenesis and treatment response. This study has two objectives; (1) characterize the population PK of omalizumab and its PD effect on IgE, and (2) develop a drug effect model of omalizumab in urticaria (via change in weekly itch severity score). The target-mediated population of PK/PD model incorporating omalizumab-IgE binding and turnover adequately described PK and PD of omalizumab. The effect compartment model and linear drug effect and additive placebo response adequately described placebo and treatment effects of omalizumab. Several baseline covariates were identified for PK/PD and drug effect models. The developed model has the potential to aid in understanding variability in PK/PD as well as response to omalizumab treatment.

Mathematical Models for Early Detection of Relapse in Multiple Myeloma Patients Treated with Bortezomib/Lenalidomide/Dexamethasone

Background: Multiple Myeloma (MM, an incurable bone marrow plasma cell malignancy, is the second most common hematological malignancy in the United States. The introduction of novel triplet-based induction therapy with bortezomib (Bor), lenalidomide (Len), and dexamethasone (Dex) has shown excellent results in several large, randomized phase III trials and is considered a standard of care for newly diagnosed MM (1-3). The question lies at which timing a clinician should move from VRd combination therapy to auto-HCT and sequential second-line therapy. Guidelines recommend waiting for International Myeloma Working Group Uniform Response Criteria (IMWG Criteria) to be met before proceeding to the next line of therapy. A tool to anticipate progression before IMWG Criteria may allow early switch to the next line therapy while the disease burden is relatively low (4). We aimed to evaluate predictive accuracy of a mathematical model to anticipate relapse 6 months before the IMWG Criteria are met. Methods: Four hundred seventy patients initially treated with a subset of VRd and with ≥ 3 serum M-protein values prior to auto-HCT, including one baseline value ≥ 0.5g/dL in the CoMMpass study (IA16) and were randomly split 2:1 into training and testing sets. A model of M-protein dynamics was developed using the training set. By fitting the model to each observation window in the testing set, distribution of future response trajectories was created for the whole population utilizing Bayesian parameter estimation, and by combining all the M-protein estimated trajectories, Receiver Operating Characteristics (ROC) curve was generated. As a comparator, we also tested most M-protein "velocity" through the two most recent time points as a predictor. Results: The two-population tumor growth inhibition model with additive drug effect and transit model for cell killing was identified to be the best model for both training and testing set. The model identified baseline R-ISS staging on M-protein baseline (M0), 1st response treatment outcome of first 2 cycles, which we will gain information before 3 months protein value data are gained, on frequency of resistant cell population (ϕ), and existence of t(4;14) mutation-positive cells on resistant cell function (dRes/dt) as the best predictor of serum M-protein kinetics. The ROC area under the curve (AUC) value of relapse prediction 180 days ahead of observed relapse by FPC was 0.829 with sensitivity and specificity both of 78% using an observation time of least 360 days, which was superior to the M-protein velocity ROC score of 0.713 under the same conditions (Fig 1B). ROC AUC continues to improve, and maintains its advantage over M-protein velocity, with each additional 90 days of observation time. Conclusion: The individualized model-based approach from data within the first 9 or more months of VRd treatment could provide prospective and reasonable predictions of future M-protein trajectories in individuals with MM. It allowed predictions for personalized "progression free" curve under various treatment scenarios. This model may enable a future study testing the difference in patient outcomes when treatment is switched when relapse is imminent versus already occurred. Reference: (1) Rosiñol, L. et al. Bortezomib, lenalidomide, and dexamethasone as induction therapy prior to autologous transplant in multiple myeloma. Blood134, 1337-45 (2019). (2) Durie, B.G.M. et al. Bortezomib with lenalidomide and dexamethasone versus lenalidomide and dexamethasone alone in patients with newly diagnosed myeloma without intent for immediate autologous stem-cell transplant (SWOG S0777): a randomised, open-label, phase 3 trial. Lancet389, 519-27 (2017). (3) Attal, M. et al. Lenalidomide, Bortezomib, and Dexamethasone with Transplantation for Myeloma. N Engl J Med376, 1311-20 (2017). (4) Jackson, G. et al. Response-adapted intensification with cyclophosphamide, bortezomib, and dexamethasone versus no intensification in patients with newly diagnosed multiple myeloma (Myeloma XI): a multicentre, open-label, randomised, phase 3 trial. Lancet Haematol.6, e616-29 (2019). Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Model enhanced reinforcement learning to enable precision dosing: A theoretical case study with dosing of propofol.

Extending the potential of precision dosing requires evaluating methodologies offering more flexibility and higher degree of personalization. Reinforcement learning (RL) holds promise in its ability to integrate multidimensional data in an adaptive process built toward efficient decision making centered on sustainable value creation. For general anesthesia in intensive care units, RL is applied and automatically adjusts dosing through monitoring of patient's consciousness. We further explore the problem of optimal control of anesthesia with propofol by combining RL with state-of-the-art tools used to inform dosing in drug development. In particular, we used pharmacokinetic-pharmacodynamic (PK-PD) modeling as a simulation engine to generate experience from dosing scenarios, which cannot be tested experimentally. Through simulations, we show that, when learning from retrospective trial data, more than 100 patients are needed to reach an accuracy within the range of what is achieved with a standard dosing solution. However, embedding a model of drug effect within the RL algorithm improves accuracy by reducing errors to target by 90% through learning to take dosing actions maximizing long-term benefit. Data residual variability impacts accuracy while the algorithm efficiently coped with up to 50% interindividual variability in the PK and 25% in the PD model's parameters. We illustrate how extending the state definition of the RL agent with meaningful variables is key to achieve high accuracy of optimal dosing policy. These results suggest that RL constitutes an attractive approach for precision dosing when rich data are available or when complemented with synthetic data from model-based tools used in model-informed drug development.

High throughput measurement of hERG drug block kinetics using the CiPA dynamic protocol

The Comprehensive in vitro Proarrhythmic Assay (CiPA) has promoted use of in silico models of drug effects on cardiac repolarization to improve proarrhythmic risk prediction. These models contain a pharmacodynamic component describing drug binding to hERG channels that required in vitro data for kinetics of block, in addition to potency, to constrain them. To date, development and validation has been undertaken using data from manual patch-clamp. The application of this approach at scale requires the development of a high-throughput, automated patch-clamp (APC) implementation. Here, we present a comprehensive analysis of the implementation of the Milnes, or CiPA dynamic protocol, on an APC platform, including quality control and data analysis. Kinetics and potency of block were assessed for bepridil, cisapride, terfenadine and verapamil with data retention/QC pass rate of 21.8% overall, or as high as 50.4% when only appropriate sweep lengths were considered for drugs with faster kinetics. The variability in IC50 and kinetics between manual and APC was comparable to that seen between sites/platforms in previous APC studies of potency. Whilst the experimental success is less than observed in screens of potency alone, it is still significantly greater than manual patch. With the modifications to protocol design, including sweep length, number of repetitions, and leak correction recommended in this study, this protocol can be applied on APC to acquire data comparable to manual patch clamp.

Exposure-response analysis of adverse events associated with molibresib and its active metabolites in patients with solid tumors.

Molibresib (GSK525762) is an investigational orally bioavailable small‐molecule bromodomain and extraterminal (BET) protein inhibitor for the treatment of advanced solid tumors. In the first‐time‐in‐human BET115521 study of molibresib in patients with solid tumors, thrombocytopenia was the most frequent treatment‐related adverse event (AE), QT prolongation was an AE of special interest based on preclinical signals, and gastrointestinal (GI) AEs (nausea, vomiting, diarrhea, and dysgeusia) were often observed. The aims of this analysis were the following: (i) develop a population pharmacokinetic (PK)/pharmacodynamic (PD) model capable of predicting platelet time courses in individual patients after administration of molibresib and identify covariates of clinical interest; (ii) evaluate the effects of molibresib (and/or its two active metabolites [GSK3529246]) exposure on cardiac repolarization by applying a systematic modeling approach using high‐quality, intensive, PK time‐matched 12‐lead electrocardiogram measurements; (iii) evaluate the exposure–response (ER) relationship between molibresib and/or GSK3529246 exposures and the occurrence of Grade 2 or higher GI AEs. Overall, the PK/PD model (including a maximal drug effect model and molibresib concentration) adequately described platelet counts following molibresib treatment and was used to simulate the impact of molibresib dosing on thrombocytopenia at different doses and regimens. ER analyses showed no clinically meaningful QT interval prolongation with molibresib at up to 100 mg q.d., and no strong correlation between molibresib exposure and the occurrence of Grade 2 or higher GI AEs. The models described here can aid dosing/schedule and drug combination strategies and may support a thorough QT study waiver request for molibresib.

Network-based modeling of drug effects on disease module in systemic sclerosis

The network-based proximity between drug targets and disease genes can provide novel insights regarding the repercussions, interplay, and repositioning of drugs in the context of disease. Current understanding and treatment for reversing of the fibrotic process is limited in systemic sclerosis (SSc). We have developed a network-based analysis for drug effects that takes into account the human interactome network, proximity measures between drug targets and disease-associated genes, genome-wide gene expression and disease modules that emerge through pertinent analysis. Currently used and potential drugs showed a wide variation in proximity to SSc-associated genes and distinctive proximity to the SSc-relevant pathways, depending on their class and targets. Tyrosine kinase inhibitors (TyKIs) approach disease gene through multiple pathways, including both inflammatory and fibrosing processes. The SSc disease module includes the emerging molecular targets and is in better accord with the current knowledge of the pathophysiology of the disease. In the disease-module network, the greatest perturbing activity was shown by nintedanib, followed by imatinib, dasatinib, and acetylcysteine. Suppression of the SSc-relevant pathways and alleviation of the skin fibrosis was remarkable in the inflammatory subsets of the SSc patients receiving TyKI therapy. Our results show that network-based drug-disease proximity offers a novel perspective into a drug’s therapeutic effect in the SSc disease module. This could be applied to drug combinations or drug repositioning, and be helpful guiding clinical trial design and subgroup analysis.

Model-based Prediction of the Long-term Glucose-Lowering Effects of Ipragliflozin, a Selective Sodium-Glucose Cotransporter 2 (SGLT2) Inhibitor, in Patients with Type 2 Diabetes Mellitus.

IntroductionSodium-dependent glucose cotransporter 2 (SGLT2) inhibitors inhibit the reabsorption of glucose from the kidneys and increase urinary glucose excretion (UGE), thereby lowering the blood glucose concentration in people suffering from type 1 and type 2 diabetes mellitus (T2DM). In a previous study, we reported a pharmacokinetics/pharmacodynamics model to estimate individual change in UGE (ΔUGE), which is a direct pharmacological effect of SGLT2 inhibitors. In this study, we report our enhancement of the previous model to predict the long-term effects of ipragliflozin on clinical outcomes in patients with T2DM.MethodsThe time course of fasting plasma glucose (FPG) and hemoglobin A1c (HbA1c) in patients with T2DM following ipragliflozin treatment that had been observed in earlier clinical trials was modeled using empirical models combined with the maximum drug effect (Emax) model and disease progression model. As a predictive factor of drug effect, estimated ΔUGE was introduced into the Emax model, instead of ipragliflozin exposure. The developed models were used to simulate the time course of FPG and HbA1c following once-daily treatment with placebo or ipragliflozin at doses of 12.5, 25, 50 and 100 mg, and the changes at 52 weeks at the approved dose of 50 mg were summarized by renal function category.ResultsThe developed models that included UGE as a dependent variable of response were found to well describe observed time courses in FPG and HbA1c. Baseline blood glucose level and renal function had significant effects on the glucose-lowering effect of ipragliflozin, and these models enabled quantification of these impacts on clinical outcomes. Simulated median changes in HbA1c in T2DM patients with mild and moderate renal impairment were 25 and 63% lower, respectively, than those in T2DM patients with normal renal function. These results are consistent with the observed clinical data from a previous renal impairment study.ConclusionsEmpirical models established based on the effect of UGE well predicted the renal function-dependent long-term glucose-lowering effects of ipragliflozin in patients with T2DM.Electronic supplementary materialThe online version of this article (10.1007/s13300-020-00785-2) contains supplementary material, which is available to authorized users.

Understanding the drug exposure-response relationship of bedaquiline to predict efficacy for novel dosing regimens in the treatment of multidrug-resistant tuberculosis.

AimsTo externally validate an earlier characterized relationship between bedaquiline exposure and decline in bacterial load in a more difficult‐to‐treat patient population, and to explore the performances of alternative dosing regimens through simulations.MethodsThe bedaquiline exposure–response relationship was validated using time‐to‐positivity data from 233 newly diagnosed or treatment‐experienced patients with drug‐resistant tuberculosis from the C209 open‐label study. The significance of the exposure–response relationship on the bacterial clearance was compared to a constant drug effect model. Tuberculosis resistance type and the presence and duration of antituberculosis pre‐treatment were evaluated as additional covariates. Alternative dosing regimens were simulated for tuberculosis patients with different types of drug resistance.ResultsHigh bedaquiline concentrations were confirmed to be associated with faster bacterial load decline in patients, given that the exposure–effect relationship provided a significantly better fit than the constant drug effect (relative likelihood = 0.0003). The half‐life of bacterial clearance was identified to be 22% longer in patients with pre‐extensively drug‐resistant (pre‐XDR) tuberculosis (TB) and 86% longer in patients with extensively drug‐resistant (XDR) TB, compared to patients with multidrug‐resistant (MDR) TB. Achievement of the same treatment response for (pre‐)XDR TB patients as for MDR TB patients would be possible by adjusting the dose and dosing frequency. Furthermore, daily bedaquiline administration as in the ZeNix regimen, was predicted to be as effective as the approved regimen.ConclusionThe confirmed bedaquiline exposure–response relationship offers the possibility to predict efficacy under alternative dosing regimens, and provides a useful tool for potential treatment optimization.

Improved Computational Identification of Drug Response Using Optical Measurements of Human Stem Cell Derived Cardiomyocytes in Microphysiological Systems.

Cardiomyocytes derived from human induced pluripotent stem cells (hiPSC-CMs) hold great potential for drug screening applications. However, their usefulness is limited by the relative immaturity of the cells’ electrophysiological properties as compared to native cardiomyocytes in the adult human heart. In this work, we extend and improve on methodology to address this limitation, building on previously introduced computational procedures which predict drug effects for adult cells based on changes in optical measurements of action potentials and Ca2+ transients made in stem cell derived cardiac microtissues. This methodology quantifies ion channel changes through the inversion of data into a mathematical model, and maps this response to an adult phenotype through the assumption of functional invariance of fundamental intracellular and membrane channels during maturation. Here, we utilize an updated action potential model to represent both hiPSC-CMs and adult cardiomyocytes, apply an IC50-based model of dose-dependent drug effects, and introduce a continuation-based optimization algorithm for analysis of dose escalation measurements using five drugs with known effects. The improved methodology can identify drug induced changes more efficiently, and quantitate important metrics such as IC50 in line with published values. Consequently, the updated methodology is a step towards employing computational procedures to elucidate drug effects in adult cardiomyocytes for new drugs using stem cell-derived experimental tissues.

Comparison of three common whole blood platelet function tests for in vitro P2Y12 induced platelet inhibition

In the context of interventional cardiology, platelet function testing may identify patients treated with P2Y12-inhibitors at an increased risk of mortality, thrombosis and bleeding. Several whole blood point-of-care platelet function analyzers are available; however, inter-device differences have not been examined systematically. To compare three platelet function tests under standardized in vitro conditions. Healthy volunteer (n = 10) blood samples were spiked with increasing concentrations of ticagrelor (0–7500 ng/mL) and/or ASA (0–3280 ng/mL), measured on three platelet function analyzers (TEG®6s, Multiplate®, and VerifyNow®) and respective Effective Concentration (EC) levels EC10, EC50 and EC90 were calculated. Repeatability was assessed in a separate group of pooled blood samples (n = 10) spiked with ticagrelor at EC10, EC50 and EC90. ASA had no impact on ADP-activated channels for all three devices. TEG®6s was able to distinguish (p ≤ 0.05) between all ticagrelor EC zones; VerifyNow® and Multiplate® were able to distinguish between three and two zones, respectively. Multiplate® showed the largest window between EC10 and EC90 (19–9153 ng/mL), followed by TEG®6s (144–2589 ng/mL), and VerifyNow® (191–1100 ng/mL). Drug effect models distribution of disagreements were identified for TEG®6s (5.0%), VerifyNow® (8.3%), and Multiplate® (13.3%). TEG®6s showed the smallest average coefficient of variation between EC conditions (5.1%), followed by Multiplate® (14.1%), and VerifyNow® (17.7%). Linear models could be generated between TEG®6s and Multiplate®, but not VerifyNow®. Significant differences were found between whole blood point-of-care platelet function analyzers and the clinical impact of these differences needs to be further investigated.

Predictive modeling of drug effects on electrocardiograms

Whole electrocardiogram (ECG) waveform analysis is a technique for evaluating aggregate arrhythmic risks of drugs. In this paper, we propose methods for exploring changes to ECG morphology due to drug effects using Gaussian model parameters, and predict patient specific post-drug ECG based on pre-drug ECG. We evaluate the proposed methods using clinical ECG recordings from subjects under the effect of anti-arrhythmic drugs Dofetilide, Quinidine, Ranolazine, and Verapamil, from the ECGRVDQ database on PhysioNet. Paired-sample t-test p-values (>0.05) suggest the proposed method can achieve similar results when compared to expert annotated J to Tpeak and Tpeak to Tend intervals for all four drug states. We employed a leave-one-out cross validation strategy to train the prediction model and produce the results. Mean Pearson correlations between all predicted and recorded post-drug waveform morphologies for all drug states across both the vector magnitude lead and Lead II is 0.94±0.05, with p-values <0.01 for all predictions; indicating significant predictions. Parameters from ECG models with Gaussian basis can be used to calculate clinically useful information and to capture or predict changes in cardiac signals due to drug effects.

A population analysis of the DGAT1 inhibitor GSK3008356 and its effect on endogenous and meal-induced triglyceride turnover in healthy subjects.

Non-alcoholic steatohepatitis (NASH) is a liver disease in which fatty infiltration is accompanied by liver inflammation. GSK3008356 is under development as a selective inhibitor of diacylglycerol acyltransferase 1 (DGAT1), a key enzyme involved in the formation of triglyceride (TG). Decreased DGAT1 activity can reduce circulating TG and liver TG, and therefore could potentially prevent or treat NASH. The aim of the current study was to develop a population pharmacokinetic-pharmacodynamic (PKPD) model that characterizes the PK disposition of GSK3008356 and its relation to the changes in blood TG. Drug concentrations were measured in 104 healthy adults receiving various single (SD) and repeat doses (RD) in a first time in human (FiH) study. A 30% fat meal was given at hour 2 postdose, and blood postprandial TG concentrations were measured at various time points. The population PKPD model consists of several parts including a PK model, drug effect model, meal effect model, and a turnover model. The pharmacokinetic data were described using a 3-compartment model. Drug effect was described by an inhibitory sigmoidal Emax model. Since TG levels change with the introduction of a meal, a bi-exponential meal effect model was utilized. The total change in TG was fitted using a turnover model with drug and meal effects on the TG production rate. The current analysis presents a PKPD modeling strategy of time-varying TG data coming from both endogenous and exogenous sources. In general, the presented model could be utilized in the model-based drug development of drugs that influence TG levels in blood.

Optimal control for a mathematical model for anti-angiogenic treatment with Michaelis-Menten pharmacodynamics

We analyze the structure of optimal protocols for a mathematical model of tumor anti-angiogenic treatment. The control represents the concentration of the agent and we consider the problem to administer an a priori given total amount of agents in order to achieve a minimum tumor volume/maximum tumor reduction. In earlier work, this problem was studied with a log-kill type pharmacodynamic model for drug effects which does not account for saturation of the drug concentration. Here we study the effect of incorporating a Michaelis-Menten (MM) or \begin{document}$ E_{\max} $\end{document} -type pharmacodynamic model, the most commonly used model in the field of pharmacometrics. We compare the formulations of both problems and the resulting solutions. The reformulated problem with \begin{document}$ E_{\max} $\end{document} pharmacodynamics is no longer linear in the control. This results in qualitative changes in the structure of optimal controls which, in line with an interpretation as concentrations, now are continuous while discontinuities exist if the log-kill model is used which is more in line with an interpretation of the control as dose rates. In spite of these qualitative differences, similarities in the structures of solutions can be observed. Both aspects are discussed theoretically and illustrated numerically.