Population pharmacokinetics, enzyme occupancy, and pharmacodynamic modeling of soticlestat in patients with developmental and epileptic encephalopathies.

Soticlestat (TAK-935) is a first-in-class, selective inhibitor of cholesterol 24-hydroxylase (CH24H) under phase III development for the treatment of the developmental and epileptic encephalopathies (DEEs), Dravet syndrome (DS), and Lennox-Gastaut syndrome (LGS). A previous model characterized the pharmacokinetics (PKs), CH24H enzyme occupancy (EO), and pharmacodynamics (PDs) of soticlestat in healthy volunteers. The present study extended this original model for patients with DEEs and investigated sources of variability. Model-based simulations were carried out to optimize dosing strategies for use in clinical trials. Data from eight phase I and II trials of healthy volunteers or patients with DEEs receiving oral soticlestat 15-1350 mg were included, encompassing 218 individuals for population PK (PopPK) analyses and 306 individuals for PK/PD analyses. Dosing strategies were identified through model-based simulations. The final mixed-effect PopPK/EO/PD model consisted of a two-compartment PK model and an effect-site compartment in the PK/EO model; soticlestat concentrations at the effect site were linked to 24S-hydroxycholesterol plasma concentrations using a semimechanistic inhibitory indirect response model. Covariates were included to account for sources of variability. Pediatric dosing strategies were developed for four body weight bands (10 to <15, 15 to <30, 30 to <45, and 45-100 kg) to account for covariate effects by body weight. The final PopPK and PK/EO/PD models accurately described PK, EO, and PD profiles of soticlestat in healthy volunteers and patients with DEEs. Covariate analyses and model-based simulations facilitated optimization of phase III trial dosing strategies for patients with DS or LGS.

A mechanistic pharmacokinetic model for intrathecal administration of antisense oligonucleotides.

Intrathecal administration is an important mode for delivering biological agents targeting central nervous system (CNS) diseases. However, current clinical practices lack a sound theorical basis for a quantitative understanding of the variables and conditions that govern the delivery efficiency and specific tissue targeting especially in the brain. This work presents a distributed mechanistic pharmacokinetic model (DMPK) for predictive analysis of intrathecal drug delivery to CNS. The proposed DMPK model captures the spatiotemporal dispersion of antisense oligonucleotides (ASO) along the neuraxis over clinically relevant time scales of days and weeks as a function of infusion, physiological and molecular properties. We demonstrate its prediction capability using biodistribution data of antisense oligonucleotide (ASO) administration in non-human primates. The results are in close agreement with the observed ASO pharmacokinetics in all key compartments of the central nervous system. The model enables determination of optimal injection parameters such as intrathecal infusion volume and duration for maximum ASO delivery to the brain. Our quantitative model-guided analysis is suitable for identifying optimal parameter settings to target specific brain regions with therapeutic drugs such as ASOs.

A Quantitative Systems Pharmacology Model Describing the Cellular Kinetic-Pharmacodynamic Relationship for a Live Biotherapeutic Product to Support Microbiome Drug Development.

Live biotherapeutic products (LBPs) are human microbiome therapies showing promise in the clinic for a range of diseases and conditions. Describing the kinetics and behavior of LBPs poses a unique modeling challenge because, unlike traditional therapies, LBPs can expand, contract, and colonize the host digestive tract. Here, we present a novel cellular kinetic-pharmacodynamic quantitative systems pharmacology model of an LBP. The model describes bacterial growth and competition, vancomycin effects, binding and unbinding to the epithelial surface, and production and clearance of butyrate as a therapeutic metabolite. The model is calibrated and validated to published data from healthy volunteers. Using the model, we simulate the impact of treatment dose, frequency, and duration as well as vancomycin pretreatment on butyrate production. This model enables model-informed drug development and can be used for future microbiome therapies to inform decision making around antibiotic pretreatment, dose selection, loading dose, and dosing duration.

Population pharmacokinetics, enzyme occupancy, and 24S-hydroxycholesterol modeling of soticlestat, a novel cholesterol 24-hydroxylase inhibitor, in healthy adults.

Soticlestat is a first-in-class, selective inhibitor of cholesterol 24-hydroxylase (CH24H), which catabolizes cholesterol to 24S-hydroxycholesterol (24HC) in the brain, in phase III development for Dravet syndrome and Lennox-Gastaut syndrome treatment. This study aimed to develop a model of soticlestat pharmacokinetics (PKs) and pharmacodynamics (PDs) using 24HC plasma concentrations and CH24H enzyme occupancy (EO) time profiles. Subsequently, model-based simulations were conducted to identify dosing strategies for phase II trials in children and adults with developmental and epileptic encephalopathies (DEEs). Four phase I trials of healthy adults involving oral administration of soticlestat 15-1350 mg were used to develop the mixed-effect population PK/EO/PD model. The population PK analysis utilized 1727 observations (104 individuals), PK/EO analysis utilized 20 observations (11 individuals), and PK/PD analysis utilized 2270 observations (99 individuals). Optimal dosing strategies were identified from model-based PK, EO, and PD simulations. The PK/EO/PD model described the observed data well and comprised a two-compartment model with dose as a covariate on peripheral volume, linear elimination, and intercompartmental clearance. Transit and effect-site compartments were included to accommodate different dosage forms and the delay between plasma drug concentrations and EO. Model-based simulations indicated that soticlestat 100-300 mg twice daily may be an optimal adult dosing regimen with weight-adjusted pediatric dosing strategies identified for evaluation in phase II trials. The population PK/EO/PD model provided understanding of the soticlestat PK/PD relationship with partial delineation of sources of variability, and identified dosing strategies for phase II trials of children and adults with DEEs.

Application of a patient-centered reverse translational systems-based approach to understand mechanisms of an adverse drug reaction of immune checkpoint inhibitors.

Immunotherapy became a key pillar of cancer therapeutics with the approvals of ipilimumab, nivolumab, and pembrolizumab, which inhibit either cytotoxic T-lymphocyte antigen-4 (CTLA-4) or programmed death-1 (PD-1) that are negative regulators of T-cell activation. However, boosting T-cell activation is often accompanied by autoimmunity, leading to adverse drug reactions (ADRs), including high grade 3-4 colitis and its severe complications whose prevalence may reach 14% for combination checkpoint inhibitors. In this research, we investigated how mechanistic differences between anti-CTLA-4 (ipilimumab) and anti-PD-1 (nivolumab and pembrolizumab) affect colitis, a general class toxicity. The data analytical platform Molecular Health Effect was utilized to map population ADR data from the US Food and Drug Administration (FDA) Adverse Event Reporting System to chemical and biological databases for hypothesis generation regarding the underlying molecular mechanisms causing colitis. Disproportionality analysis was used to assess the statistical relevance between adverse events of interest and molecular causation. We verified that the anti-CTLA-4 drug is associated with an approximately three-fold higher proportional reporting ratio associated with colitis than those of the anti-PD-1 drugs. The signal of the molecular mechanisms, including signaling pathways of inflammatory cytokines, was statistically insignificant to test the hypothesis that the severer rate of colitis associated with ipilimumab would be due to a greater magnitude of T-cell activation as a result of earlier response of the anti-CTLA-4 drug in the immune response. This patient-centered systems-based approach provides an exploratory process to better understand drug pair adverse events at pathway and target levels through reverse translation from postmarket surveillance safety reports.

A case study of a patient-centered reverse translational systems-based approach to understand adverse event profiles in drug development.

Adverse drug reactions (ADRs) of targeted therapy drugs (TTDs) are frequently unexpected and long-term toxicities detract from exceptional efficacy of new TTDs. In this proof-of-concept study, we explored how molecular causation involved in trastuzumab-induced cardiotoxicity changes when trastuzumab was given in combination with doxorubicin, tamoxifen, paroxetine, or lapatinib. The data analytical platform Molecular Health Effect was utilized to map population ADR data from the US Food and Drug Administration (FDA) Adverse Event Reporting System to chemical and biological databases (such as UniProt and Reactome), for hypothesis generation regarding the underlying molecular mechanisms causing cardiotoxicity. Disproportionality analysis was used to assess the statistical relevance between adverse events of interest and molecular causation. Literature search was performed to compare the established hypotheses to published experimental findings. We found that the combination therapy of trastuzumab and doxorubicin may affect mitochondrial dysfunction in cardiomyocytes through different molecular pathways such as BCL-X and PGC-1α proteins, leading to a synergistic effect of cardiotoxicity. We found, on the other hand, that trastuzumab-induced cardiotoxicity would be diminished by concomitant use of tamoxifen, paroxetine, and/or lapatinib. Tamoxifen and paroxetine may cause less cardiotoxicity through an increase in antioxidant activities, such as glutathione conjugation. Lapatinib may decrease the apoptotic effects in cardiomyocytes by altering the effects of trastuzumab on BCL-X proteins. This patient-centered systems-based approach provides, based on the trastuzumab-induced ADR cardiotoxicity, an example of how to apply reverse translation to investigate ADRs at the molecular pathway and target level to understand the causality and prevalence during drug development of novel therapeutics.

Application of machine learning to predict reduction in total PANSS score and enrich enrollment in schizophrenia clinical trials.

Clinical trial efficiency, defined as facilitating patient enrollment, and reducing the time to reach safety and efficacy decision points, is a critical driving factor for making improvements in therapeutic development. The present work evaluated a machine learning (ML) approach to improve phase II or proof-of-concept trials designed to address unmet medical needs in treating schizophrenia. Diagnostic data from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) trial were used to develop a binary classification ML model predicting individual patient response as either "improvement," defined as greater than 20% reduction in total Positive and Negative Syndrome Scale (PANSS) score, or "no improvement," defined as an inadequate treatment response (<20% reduction in total PANSS). A random forest algorithm performed best relative to other tree-based approaches in model ability to classify patients after 6 months of treatment. Although model ability to identify true positives, a measure of model sensitivity, was poor (<0.2), its specificity, true negative rate, was high (0.948). A second model, adapted from the first, was subsequently applied as a proof-of-concept for the ML approach to supplement trial enrollment by identifying patients not expected to improve based on their baseline diagnostic scores. In three virtual trials applying this screening approach, the percentage of patients predicted to improve ranged from 46% to 48%, consistently approximately double the CATIE response rate of 22%. These results show the promising application of ML to improve clinical trial efficiency and, as such, ML models merit further consideration and development.

Characterizing Effects of Antidiabetic Drugs on Heart Rate, Systolic and Diastolic Blood Pressure.

A model-based meta-analysis was performed with reported data from obese subjects and patients with type 2 diabetes (T2DM) to characterize the effects of dipeptidyl peptidase 4 (DPP4) inhibitors, gastric inhibitory polypeptides (GIPs), glucagon-like peptide-1 (GLP1), and dual GIP/GLP1 agonists, or a combination of these antidiabetic drugs (ADs) on heart rate (HR), diastolic blood pressure (DBP), and systolic blood pressure (SBP). A systematic literature search and review after the Cochrane method identified sources for investigational and approved ADs resulted in a comprehensive database with data from 178 clinical studies in obese subjects and patients with T2DM. Results indicated that there were AD class-dependent effects on HR and SBP, whereas no clear AD-related effects on DBP were found. All AD classes, except for DPP4 inhibitors, increased HR. The largest increase of 12 bpm was seen with GLP1 receptor agonists. All AD classes appeared to decrease SBP. DPP4 inhibitors were associated with a marginal decrease of ~ 1 mmHg, whereas GLP1 and GIP/GLP1 dual agonists exhibited the largest decrease of ~ 3 mmHg in SBP. AD-related effects were similar in obese subjects and patients with T2DM. In conclusion, there are clinically relevant AD-related effects on both HR and SBP, but not on DBP. DPP4 inhibitors are associated with the smallest (if at all) effects on HR and SBP, whereas GLP1 inhibitors exhibited the largest effects on these two cardiovascular end points. Additional studies are warranted to further investigate how AD-related SBP decreases combined with HR increases affect long-term cardiovascular mortality.

A quantitative systems pharmacology model of colonic motility with applications in drug development.

We developed a mathematical model of colon physiology driven by serotonin signaling in the enteric nervous system. No such models are currently available to assist drug discovery and development for GI motility disorders. Model parameterization was informed by published preclinical and clinical data. Our simulations provide clinically relevant readouts of bowel movement frequency and stool consistency. The model recapitulates healthy and slow transit constipation phenotypes, and the effect of a 5-HT4 receptor agonist in healthy volunteers. Using the calibrated model, we predicted the agonist dose to normalize defecation frequency in slow transit constipation while avoiding the onset of diarrhea. Model sensitivity analysis predicted that changes in HAPC frequency and liquid secretion have the greatest impact on colonic motility. However, exclusively increasing the liquid secretion can lead to diarrhea. In contrast, increasing HAPC frequency alone can enhance bowel frequency without leading to diarrhea. The quantitative systems pharmacology approach used here demonstrates how mechanistic modeling of disease pathophysiology expands our understanding of biology and supports judicious hypothesis generation for therapeutic intervention.

Prediction of Nephropathy in Type 2 Diabetes: An Analysis of the ACCORD Trial Applying Machine Learning Techniques.

Applying data mining and machine learning (ML) techniques to clinical data might identify predictive biomarkers for diabetic nephropathy (DN), a common complication of type 2 diabetes mellitus (T2DM). A retrospective analysis of the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial was intended to identify such factors using ML. The longitudinal data were stratified by time after patient enrollment to differentiate early and late predictors. Our results showed that Random Forest and Simple Logistic Regression methods exhibited the best performance among the evaluated algorithms. Baseline values for glomerular filtration rate (GFR), urinary creatinine, urinary albumin, potassium, cholesterol, low-density lipoprotein, and urinary albumin to creatinine ratio were identified as DN predictors. Early predictors were the baseline values of GFR, systolic blood pressure, as well as fasting plasma glucose (FPG) and potassium at month 4. Changes per year in GFR, FPG, and triglycerides were recognized as predictors of late development. In conclusion, ML-based methods successfully identified predictive factors for DN among patients with T2DM.

Benchmarking renin suppression and blood pressure reduction of direct renin inhibitor imarikiren through quantitative systems pharmacology modeling.

Multiple classes of antihypertensive drugs inhibit components of the renin-angiotensin-aldosterone system (RAAS). The primary physiological effector of the RAAS is angiotensin II (AngII) bound to the AT1 receptor (AT1-bound AngII). There is a strong non-linear feedback from AT1-bound AngII on renin secretion. Since AT1-bound AngII is not readily measured experimentally, plasma renin concentration (PRC) and/or activity (PRA) are typically measured to indicate RAAS suppression. We investigated the RAAS suppression of imarikiren hydrochloride (TAK-272; SCO-272), a direct renin inhibitor currently under clinical development. We employed a previously developed quantitative system pharmacology (QSP) model to benchmark renin suppression and blood pressure regulation with imarikiren compared to other RAAS therapies. A pharmacokinetic (PK) model of imarikiren was linked with the existing QSP model, which consists of a mechanistic representation of the RAAS pathway coupled with a model of blood pressure regulation and volume homeostasis. The PK and pharmacodynamic effects of imarikiren were calibrated by fitting drug concentration, PRA, and PRC data, and trough AT1-bound AngII suppression was simulated. We also prospectively simulated expected mean arterial pressure reduction in a cohort of hypertensive virtual patients. These predictions were benchmarked against predictions for several other (previously calibrated) RAAS monotherapies and dual-RAAS therapies. Our analysis indicates that low doses (5-10 mg) of imarikiren are comparable to current RAAS therapies, and at higher doses (25-200 mg), RAAS suppression may be equivalent to existing dual-RAAS combinations (at registered doses). This study illustrates application of QSP modeling to predict phase II endpoints from phase I data.

Evaluation of the Pharmacokinetics and Safety of a Single Oral Dose of Fasiglifam in Subjects with Mild or Moderate Hepatic Impairment.

BACKGROUND AND AIMS: Fasiglifam, a potent, selective novel agonist of G protein-coupled receptor 40, stimulates insulin secretion at elevated blood glucose levels in a glucose-dependent manner. This study evaluated the potential effect of hepatic impairment on the pharmacokinetics and safety of a single dose of fasiglifam and its metabolite M-I. Fasiglifam's clinical development was halted due to liver safety concerns. METHODS: In this phase I, open-label study, subjects with mild or moderate hepatic impairment, along with matched controls (gender, weight, age, and smoking status), received a single, 25-mg oral dose of fasiglifam. Blood samples were collected through 336 h post-dose for pharmacokinetic evaluation. RESULTS: Overall, 73% of subjects were male with a mean age of 54 years. Compared with normal hepatic function subjects (n = 14), mean systemic fasiglifam exposure (Cmax and AUC∞) was reduced in mild (n = 8) and moderate (n = 8) hepatic impairment subjects by approximately 20-40%. However, the observed percent unbound drug plasma concentration appeared comparable across all groups. Mean oral clearance was higher and terminal half-life lower in subjects with mild or moderate hepatic impairment compared with normal hepatic function subjects. Fasiglifam M-I systemic exposure increased by approximately twofold in subjects with mild or moderate hepatic impairment compared with those with normal hepatic function. Fasiglifam was well tolerated, and there were no reports of hypoglycemia. CONCLUSION: Hepatic status did not significantly impact systemic exposure of fasiglifam in this study, in fact, a decrease was observed, suggesting no dose reduction would be required for patients with hepatic impairment.

Model-Based Approach to Predict Adherence to Protocol During Antiobesity Trials.

Development of antiobesity drugs is continuously challenged by high dropout rates during clinical trials. The objective was to develop a population pharmacodynamic model that describes the temporal changes in body weight, considering disease progression, lifestyle intervention, and drug effects. Markov modeling (MM) was applied for quantification and characterization of responder and nonresponder as key drivers of dropout rates, to ultimately support the clinical trial simulations and the outcome in terms of trial adherence. Subjects (n = 4591) from 6 Contrave® trials were included in this analysis. An indirect-response model developed by van Wart et al was used as a starting point. Inclusion of drug effect was dose driven using a population dose- and time-dependent pharmacodynamic (DTPD) model. Additionally, a population-pharmacokinetic parameter- and data (PPPD)-driven model was developed using the final DTPD model structure and final parameter estimates from a previously developed population pharmacokinetic model based on available Contrave® pharmacokinetic concentrations. Last, MM was developed to predict transition rate probabilities among responder, nonresponder, and dropout states driven by the pharmacodynamic effect resulting from the DTPD or PPPD model. Covariates included in the models and parameters were diabetes mellitus and race. The linked DTPD-MM and PPPD-MM was able to predict transition rates among responder, nonresponder, and dropout states well. The analysis concluded that body-weight change is an important factor influencing dropout rates, and the MM depicted that overall a DTPD model-driven approach provides a reasonable prediction of clinical trial outcome probabilities similar to a pharmacokinetic-driven approach.

Fasiglifam (TAK-875) Alters Bile Acid Homeostasis in Rats and Dogs: A Potential Cause of Drug Induced Liver Injury.

Fasiglifam (TAK-875), a Free Fatty Acid Receptor 1 (FFAR1) agonist in development for the treatment of type 2 diabetes, was voluntarily terminated in phase 3 due to adverse liver effects. A mechanistic investigation described in this manuscript focused on the inhibition of bile acid (BA) transporters as a driver of the liver findings. TAK-875 was an in vitro inhibitor of multiple influx (NTCP and OATPs) and efflux (BSEP and MRPs) hepatobiliary BA transporters at micromolar concentrations. Repeat dose studies determined that TAK-875 caused a dose-dependent increase in serum total BA in rats and dogs. Additionally, there were dose-dependent increases in both unconjugated and conjugated individual BAs in both species. Rats had an increase in serum markers of liver injury without correlative microscopic signs of tissue damage. Two of 6 dogs that received the highest dose of TAK-875 developed liver injury with clinical pathology changes, and by microscopic analysis had portal granulomatous inflammation with neutrophils around a crystalline deposition. The BA composition of dog bile also significantly changed in a dose-dependent manner following TAK-875 administration. At the highest dose, levels of taurocholic acid were 50% greater than in controls with a corresponding 50% decrease in taurochenodeoxycholic acid. Transporter inhibition by TAK-875 may cause liver injury in dogs through altered bile BA composition characteristics, as evidenced by crystalline deposition, likely composed of test article, in the bile duct. In conclusion, a combination of in vitro and in vivo evidence suggests that BA transporter inhibition could contribute to TAK-875-mediated liver injury in dogs.

Population Pharmacokinetics and Exposure-Response of a Fixed-Dose Combination of Azilsartan Medoxomil and Chlorthalidone in Patients With Stage 2 Hypertension.

Population pharmacokinetic and exposure-response models for azilsartan medoxomil (AZL-M) and chlorthalidone (CLD) were developed using data from an 8-week placebo-controlled phase 3, factorial study of 20, 40, and 80 mg AZL-M every day (QD) and 12.5 and 25 mg CLD QD in fixed-dose combination (FDC) in subjects with moderate to severe essential hypertension. A 2-compartment model with first-order absorption and elimination was developed to describe pharmacokinetics. An Emax model for exposure-response analysis evaluated AZL-M/CLD effects on ambulatory systolic blood pressure (SBP). Estimated oral clearance and apparent volume of distribution (central compartment) were 1.47 L/h and 3.98 L for AZL, and 4.13 L/h and 62.1 L for CLD. Age as a covariate had the largest effect on AZL and CLD exposure (±20% change). Predicted maximal SBP responses (Emax ) were -15.6 and -23.9 mm Hg for AZL and CLD. Subgroup analysis identified statistically significant Emax differences for black vs nonblack subjects, whereby the reduced AZL response in black subjects was offset by greater response to CLD. The estimated Emax for AZL and CLD was generally greater in subjects with higher baseline BP. In conclusion, no dose adjustments to AZL-M or CLD are warranted based on identified covariates, and antihypertensive efficacy of AZL-M/CLD combination therapy is comparable in black and nonblack subjects.

Application of pharmacometric approaches to evaluate effect of weight and renal function on pharmacokinetics of alogliptin.

AIMS: The aims of the study were to characterize the pharmacokinetics (PK) of alogliptin in healthy and type 2 diabetes mellitus (T2DM) subjects using a population PK approach and to assess the influence of various covariates on alogliptin exposure. METHODS: Plasma concentration data collected from two phase 1 studies and one phase 3 study following administration of alogliptin (12.5-400 mg) were used for the PK model development. One- and two-compartment models were evaluated as base structural PK models. The impact of selected covariates was assessed using stepwise forward selection and backward elimination procedures. The predictability and robustness of the final model was evaluated using visual predictive check and bootstrap analyses. The final model was used to perform simulations and guide appropriate dose adjustments. RESULTS: A two-compartment model with first-order absorption and elimination best described the alogliptin concentration vs. time profiles. Creatinine clearance and weight had a statistically significant effect on the oral clearance (CL/F) of alogliptin. The model predicted a lower CL/F (17%, 35%, 80%) and a higher systemic exposure (56%, 89%, 339%) for subjects with mild, moderate and severe renal impairment, respectively, compared with healthy subjects. Effect of weight on CL/F was not considered clinically relevant. Simulations at different doses of alogliptin support the approved doses of 12.5 mg and 6.25 mg for patients with moderate and severe renal impairment, respectively. CONCLUSIONS: The PK of alogliptin was well characterized by the model. The analysis suggested an alogliptin dose adjustment for subjects with moderate-to-severe renal impairment and no dose adjustments based on weight.

A Population Pharmacokinetic-Pharmacodynamic Meta-Analysis of Vortioxetine in Patients with Major Depressive Disorder.

Vortioxetine is approved for the treatment of major depressive disorder (MDD). This analysis aimed to develop pharmacokinetic (PK) and PK/Efficacy models to evaluate the exposure-response relationship for vortioxetine in patients with MDD. PK data from 10 MDD and two generalized anxiety disorder studies of vortioxetine (3160 patients), and efficacy data [Montgomery-Åsberg Depression Rating Scale (MADRS)] from seven MDD studies (2537 patients), were used for the development of PK and PK/Efficacy models. One- and two-compartment models were evaluated as structural PK models, and linear and nonlinear (Emax) models were used to describe the relationship between average vortioxetine concentration at steady-state (Cav) and change in MADRS score from baseline (ΔMADRS). The impact of selected covariates on the PK and efficacy parameters of vortioxetine was also investigated. PK of vortioxetine was best characterized by a two-compartment model with first-order absorption and elimination. Mean estimates for oral clearance (CL/F) and volume of distribution for the central compartment of vortioxetine were 42 L/hr and 2920 L. Creatinine clearance, height and geographic region had statistically significant effects on vortioxetine CL/F, but the effect of each of these covariates was not considered clinically relevant, as they lead to ±26% change in area under the curve or Cmax of vortioxetine. An Emax model best described the relationship between ΔMADRS and Cav. Half-maximal effective concentration (EC50) and Emax estimates were 24.9 ng/mL and 7.0. No identified covariates, except region, had clinically meaningful effects on vortioxetine efficacy. These PK/Efficacy models adequately characterized the vortioxetine exposure-response relationship.

Preclinical pharmacokinetic/pharmacodynamic modeling and simulation in the pharmaceutical industry: an IQ consortium survey examining the current landscape.

The application of modeling and simulation techniques is increasingly common in preclinical stages of the drug discovery and development process. A survey focusing on preclinical pharmacokinetic/pharmacodynamics (PK/PD) analysis was conducted across pharmaceutical companies that are members of the International Consortium for Quality and Innovation in Pharmaceutical Development. Based on survey responses, ~68% of companies use preclinical PK/PD analysis in all therapeutic areas indicating its broad application. An important goal of preclinical PK/PD analysis in all pharmaceutical companies is for the selection/optimization of doses and/or dose regimens, including prediction of human efficacious doses. Oncology was the therapeutic area with the most PK/PD analysis support and where it showed the most impact. Consistent use of more complex systems pharmacology models and hybrid physiologically based pharmacokinetic models with PK/PD components was less common compared to traditional PK/PD models. Preclinical PK/PD analysis is increasingly being included in regulatory submissions with ~73% of companies including these data to some degree. Most companies (~86%) have seen impact of preclinical PK/PD analyses in drug development. Finally, ~59% of pharmaceutical companies have plans to expand their PK/PD modeling groups over the next 2 years indicating continued growth. The growth of preclinical PK/PD modeling groups in pharmaceutical industry is necessary to establish required resources and skills to further expand use of preclinical PK/PD modeling in a meaningful and impactful manner.

A Population Pharmacokinetic and Pharmacodynamic Analysis of Peginesatide in Patients with Chronic Kidney Disease on Dialysis.

Peginesatide (OMONTYS®) is an erythropoiesis-stimulating agent that was indicated in the United States for the treatment of anemia due to chronic kidney disease in adult patients on dialysis prior to its recent marketing withdrawal by the manufacturer. The objective of this analysis was to develop a population pharmacokinetic and pharmacodynamic model to characterize the time-course of peginesatide plasma and hemoglobin concentrations following intravenous and subcutaneous administration. Plasma samples (n = 2,665) from 672 patients with chronic kidney disease (on or not on dialysis) and hemoglobin samples (n = 18,857) from 517 hemodialysis patients (subset of the 672 patients), were used for pharmacokinetic-pharmacodynamic model development in NONMEM VI. The pharmacokinetic profile of peginesatide was best described by a two-compartment model with first-order absorption and saturable elimination. The relationship between peginesatide and hemoglobin plasma concentrations was best characterized by a modified precursor-dependent lifespan indirect response model. The estimate of maximal stimulatory effect of peginesatide on the endogenous production rate of progenitor cells (Emax) was 0.54. The estimate of peginesatide drug concentration required for 50% of maximal response (EC50) estimates was 0.4 µg/mL. Several significant (P<0.005) covariates affected simulated peginesatide exposure by ≤36%. Based upon ≤0.2 g/dL effects on simulated hemoglobin levels, none were considered clinically relevant.

TAK-875 versus placebo or glimepiride in type 2 diabetes mellitus: a phase 2, randomised, double-blind, placebo-controlled trial.

BACKGROUND: Activation of free fatty acid receptor 1 (FFAR1; also known as G-protein-coupled receptor 40) by fatty acids stimulated glucose-dependent ß-cell insulin secretion in preclinical models. We aimed to assess whether selective pharmacological activation of this receptor by TAK-875 in patients with type 2 diabetes mellitus improved glycaemic control without hypoglycaemia risk. METHODS: We undertook a phase 2, randomised, double-blind, and placebo-controlled and active-comparator-controlled trial in outpatients with type 2 diabetes who had not responded to diet or metformin treatment. Patients were randomly assigned equally to receive placebo, TAK-875 (6·25, 25, 50, 100, or 200 mg), or glimepiride (4 mg) once daily for 12 weeks. Patients and investigators were masked to treatment assignment. The primary outcome was change in haemoglobin A(1c) (HbA(1c)) from baseline. Analysis included all patients randomly assigned to treatment groups who received at least one dose of double-blind study drug. The trial is registered at ClinicalTrials.gov, NCT01007097. FINDINGS: 426 patients were randomly assigned to TAK-875 (n=303), placebo (n=61), and glimepiride (n=62). At week 12, significant least-squares mean reductions in HbA(1c) from baseline occurred in all TAK-875 (ranging from -1·12% [SE 0·113] with 50 mg to -0·65% [0·114] with 6·25 mg) and glimepiride (-1·05% [SE 0·111]) groups versus placebo (-0·13% [SE 0·115]; p value range 0·001 to <0·0001). Treatment-emergent hypoglycaemic events were similar in the TAK-875 and placebo groups (2% [n=7, all TAK-875 groups] vs 3% [n=2]); significantly higher rates were reported in the glimepiride group (19% [n=12]; p value range 0·010-0·002 vs all TAK-875 groups). Incidence of treatment-emergent adverse events was similar in the TAK-875 overall (49%; n=147, all TAK-875 groups) and placebo groups (48%, n=29) and was lower than in the glimepiride group (61%, n=38). INTERPRETATION: TAK-875 significantly improved glycaemic control in patients with type 2 diabetes with minimum risk of hypoglycaemia. The results show that activation of FFAR1 is a viable therapeutic target for treatment of type 2 diabetes. FUNDING: Takeda Global Research and Development.