Belantamab mafodotin concentration-QTc relationships in patients with relapsed or refractory multiple myeloma from the DREAMM-1 and -2 studies.

AIMS: To evaluate relationships between plasma concentrations of belantamab mafodotin, total monoclonal antibody, and its payload and changes in electrocardiogram (ECG) parameters in patients with relapsed or refractory multiple myeloma from the DREAMM-1 and DREAMM-2 studies. METHODS: Hysteresis plots and linear regression analyses of pharmacokinetic (PK) analyte (belantamab mafodotin, total monoclonal antibody, and cytotoxic cysteine-maleimidocaproyl monomethyl auristatin F payload) concentrations vs. time-matched ECG parameters (absolute/change from baseline in QT interval corrected for RR interval [QTc/ΔQTc] and QT interval corrected for heart rate by Fridericia's formula [QTcF/ΔQTcF]) were performed. Concentrations of PK analyte required for a 10-ms increase in QTc in DREAMM-2 were calculated via simulation, as was the probability of ΔQTc/ΔQTcF exceeding 10 ms for the expected Cmax of PK analyte concentrations associated with the doses (2.5 and 3.4 mg/kg) administered in DREAMM-2. RESULTS: Time-matched PK and ECG data from 290 patients (DREAMM-1, n = 73; DREAMM-2, n = 217) were analysed. Hysteresis plots did not clearly indicate any concentration-related prolongation in QTc or QTcF; regression analyses indicated a very small rate of increase in ΔQTc and ΔQTcF with increasing concentrations of PK analytes. Calculated concentrations of PK analyte required for a 10-ms prolongation in QTc were higher than the maximum analyte concentrations observed following treatment with belantamab mafodotin in DREAMM-2; the probability that each dose would prolong ΔQTc and ΔQTcF by >10 ms was 0 and <0.25%, respectively. CONCLUSION: This study of belantamab mafodotin and its payload did not provide evidence of QT prolongation in patients with relapsed or refractory multiple myeloma at clinically relevant doses.

The variability in beta-cell function in placebo-treated subjects with type 2 diabetes: application of the weight-HbA1c-insulin-glucose (WHIG) model.

AIM: The weight-glycosylated haemoglobin (HbA1C)-insulin-glucose (WHIG) model describes the effects of changes in weight on insulin sensitivity (IS) in newly diagnosed, obese subjects receiving placebo treatment. This model was applied to a wider population of placebo-treated subjects, to investigate factors influencing the variability in IS and ß-cell function. METHODS: The WHIG model was applied to the WHIG dataset (Study 1) and two other placebo datasets (Studies 2 and 3). Studies 2 and 3 consisted of nonobese subjects and subjects with advanced type 2 diabetes mellitus (T2DM). Body weight, fasting serum insulin (FSI), fasting plasma glucose (FPG) and HbA1c were used for nonlinear mixed-effects modelling (using NONMEM v7.2 software). Sources of interstudy variability (ISV) and potential covariates (age, gender, diabetes duration, ethnicity, compliance) were investigated. RESULTS: An ISV for baseline parameters (body weight and ß-cell function) was required. The baseline ß-cell function was significantly lower in subjects with advanced T2DM (median difference: Study 2: 15.6%, P < 0.001; Study 3: 22.7%, P < 0.001) than in subjects with newly diagnosed T2DM (Study 1). A reduction in the estimated insulin secretory response in subjects with advanced T2DM was observed but diabetes duration was not a significant covariate. CONCLUSION: The WHIG model can be used to describe the changes in weight, IS and ß-cell function in the diabetic population. IS remained relatively stable between subjects but a large ISV in ß-cell function was observed. There was a trend towards decreasing ß-cell responsiveness with diabetes duration, and further studies, incorporating subjects with a longer history of diabetes, are required.

Pooled population pharmacokinetic model of imipenem in plasma and the lung epithelial lining fluid.

AIMS: Several clinical trials have confirmed the therapeutic benefit of imipenem for treatment of lung infections. There is however no knowledge of the penetration of imipenem into the lung epithelial lining fluid (ELF), the site of action relevant for lung infections. Furthermore, although the plasma pharmacokinetics (PK) of imipenem has been widely studied, most studies have been based on selected patient groups. The aim of this analysis was to characterize imipenem plasma PK across populations and to quantify imipenem ELF penetration. METHODS: A population model for imipenem plasma PK was developed using data obtained from healthy volunteers, elderly subjects and subjects with renal impairment, in order to identify predictors for inter-individual variability (IIV) of imipenem PK. Subsequently, a clinical study which measured plasma and ELF concentrations of imipenem was included in order to quantify lung penetration. RESULTS: A two compartmental model best described the plasma PK of imipenem. Creatinine clearance and body weight were included as subject characteristics predictive for IIV on clearance. Typical estimates for clearance, central and peripheral volume, and inter-compartmental clearance were 11.5 l h(-1) , 9.37 l, 6.41 l, 13.7 l h(-1) , respectively (relative standard error (RSE) <8%). The distribution of imipenem into ELF was described using a time-independent penetration coefficient of 0.44 (RSE 14%). CONCLUSION: The identified lung penetration coefficient confirms the clinical relevance of imipenem for treatment of lung infections, while the population PK model provided insights into predictors of IIV for imipenem PK and may be of relevance to support dose optimization in various subject groups.

Structure-Based Prediction of Anti-infective Drug Concentrations in the Human Lung Epithelial Lining Fluid.

PURPOSE: Obtaining pharmacologically relevant exposure levels of antibiotics in the epithelial lining fluid (ELF) is of critical importance to ensure optimal treatment of lung infections. Our objectives were to develop a model for the prediction of the ELF-plasma concentration ratio (EPR) of antibiotics based on their chemical structure descriptors (CSDs). METHODS: EPR data was obtained by aggregating ELF and plasma concentrations from historical clinical studies investigating antibiotics and associated agents. An elastic net regularized regression model was used to predict EPRs based on a large number of CSDs. The model was tuned using leave-one-drug-out cross validation, and the predictions were further evaluated using a test dataset. RESULTS: EPR data of 56 unique compounds was included. A high degree of variability in EPRs both between- and within drugs was apparent. No trends related to study design or pharmacokinetic factors could be identified. The model predicted 80% of the within-drug variability (R(2) WDV) and 78.6% of drugs were within 3-fold difference from the observations. Key CSDs were related to molecular size and lipophilicity. When predicting EPRs for a test dataset the R(2) WDV was 75%. CONCLUSIONS: This model is of relevance to inform dose selection and optimization during antibiotic drug development of agents targeting lung infections.

Interplay between α-, ß-, and γ-secretases determines biphasic amyloid-ß protein level in the presence of a γ-secretase inhibitor.

Amyloid-ß (Aß) is produced by the consecutive cleavage of amyloid precursor protein (APP) first by ß-secretase, generating C99, and then by γ-secretase. APP is also cleaved by α-secretase. It is hypothesized that reducing the production of Aß in the brain may slow the progression of Alzheimer disease. Therefore, different γ-secretase inhibitors have been developed to reduce Aß production. Paradoxically, it has been shown that low to moderate inhibitor concentrations cause a rise in Aß production in different cell lines, in different animal models, and also in humans. A mechanistic understanding of the Aß rise remains elusive. Here, a minimal mathematical model has been developed that quantitatively describes the Aß dynamics in cell lines that exhibit the rise as well as in cell lines that do not. The model includes steps of APP processing through both the so-called amyloidogenic pathway and the so-called non-amyloidogenic pathway. It is shown that the cross-talk between these two pathways accounts for the increase in Aß production in response to inhibitor, i.e. an increase in C99 will inhibit the non-amyloidogenic pathway, redirecting APP to be cleaved by ß-secretase, leading to an additional increase in C99 that overcomes the loss in γ-secretase activity. With a minor extension, the model also describes plasma Aß profiles observed in humans upon dosing with a γ-secretase inhibitor. In conclusion, this mechanistic model rationalizes a series of experimental results that spans from in vitro to in vivo and to humans. This has important implications for the development of drugs targeting Aß production in Alzheimer disease.

Population PKPD modeling of BACE1 inhibitor-induced reduction in Aß levels in vivo and correlation to in vitro potency in primary cortical neurons from mouse and guinea pig.

PURPOSE: The aims were to quantify the in vivo time-course between the oral dose, the plasma and brain exposure and the inhibitory effect on Amyloid ß (Aß) in brain and cerebrospinal fluid, and to establish the correlation between in vitro and in vivo potency of novel ß-secretase (BACE1) inhibitors. METHODS: BACE1-mediated inhibition of Aß was quantified in in vivo dose- and/or time-response studies and in vitro in SH-SY5Y cells, N2A cells, and primary cortical neurons (PCN). An indirect response model with inhibition on Aß production rate was used to estimate unbound in vivo IC 50 in a population pharmacokinetic-pharmacodynamic modeling approach. RESULTS: Estimated in vivo inhibitory potencies varied between 1 and 1,000 nM. The turnover half-life of Aß40 in brain was predicted to be 0.5 h in mouse and 1 h in guinea pig. An excellent correlation between PCN and in vivo potency was observed. Moreover, a strong correlation in potency was found between human SH-SY5Y cells and mouse PCN, being 4.5-fold larger in SH-SY5Y cells. CONCLUSION: The strong in vivo-in vitro correlation increased the confidence in using human cell lines for screening and optimization of BACE1 inhibitors. This can optimize the design and reduce the number of preclinical in vivo effect studies.

How debunking biases in research and development decisions could lead to more equitable healthcare?

Decades of research have demonstrated that a variety of cognitive biases can affect our judgment and ability to make rational decisions in personal and professional environments. The lengthy, risky, and costly nature of pharmaceutical research and development (R&D) makes it vulnerable to biased decision-making. Moreover, cognitive biases can play a role in regulatory and clinical decision-making, the latter impacting diagnostic and treatment decisions in the therapeutic use of medicines. These inherent and/or institutionalized biases (e.g., in assumptions, data, or decision-making practices) could conceivably contribute to health inequities. In this mini-review, we provide a broad perspective on how cognitive biases can affect pharmaceutical R&D, regulatory evaluation, and therapeutic decision-making. Example approaches to mitigate the effect of common biases in the development, approval, and use of new therapeutics, such as quantitative decision criteria, multidisciplinary reviews, regulatory and treatment guidelines, and evidence-based clinical decision support systems are illustrated. Mitigating the impact of cognitive biases could increase pharma R&D efficiency, change the perspective and prioritization of unmet medical needs, increase representativeness and quality of evidence generated through clinical trials and real-world research, leading to higher quality insights and more effective medication use, and as such could eventually contribute to more equitable healthcare.

In vivo and ex vivo inhibition of spinal nerve ligation-induced ectopic activity by sodium channel blockers correlate to in vitro inhibition of NaV1.7 and clinical efficacy: a pharmacokinetic-pharmacodynamic translational approach.

PURPOSE: In vivo and ex vivo inhibition of ectopic activity of clinically used and newly developed sodium channel (NaV) blockers were quantified in the rat spinal nerve ligation (SNL) model using a pharmacokinetic-pharmacodynamic (PKPD) approach and correlated to in vitro NaV1.7 channel inhibition and clinical effective concentrations. METHODS: In vivo, drug exposure and inhibition of ectopic activity were assessed in anaesthetized SNL rats at two dose levels. Ex vivo, compounds were applied at increasing concentrations to dorsal root ganglias isolated from SNL rats. The inhibitory potency (IC 50 ) was estimated using PKPD analysis. In vitro IC 50 was estimated using an electrophysiology-based assay using recombinant rat and human NaV1.7 expressing HEK293 cells. RESULTS: In vivo and ex vivo inhibition of ectopic activity correlated well with the in vitro inhibition on the rat NaV1.7 channel. The estimated IC 50s for inhibition of ectopic activity in the SNL model occurred at similar unbound concentrations as clinical effective concentrations in humans. CONCLUSIONS: Inhibition of ectopic activity in the SNL model could be useful in predicting clinical effective concentrations for novel sodium channel blockers. In addition, in vitro potency could be used for screening, characterization and selection of compounds, thereby reducing the need for in vivo testing.

Systems pharmacology modeling of drug-induced modulation of thyroid hormones in dogs and translation to human.

PURPOSE: To develop a systems pharmacology model based on hormone physiology and pharmacokinetic-pharmacodynamic concepts describing the impact of thyroperoxidase (TPO) inhibition on thyroid hormone homeostasis in the dog and to predict drug-induced changes in thyroid hormones in humans. METHODS: A population model was developed based on a simultaneous analysis of concentration-time data of T4, T3 and TSH in dogs following once daily oral dosing for up to 6-months of a myeloperoxidase inhibitor (MPO-IN1) with TPO inhibiting properties. The model consisted of linked turnover compartments for T4, T3 and TSH including a negative feedback from T4 on TSH concentrations. RESULTS: The model could well describe the concentration-time profiles of thyroid hormones in dog. Successful model validation was performed by predicting the hormone concentrations during 1-month administration of MPO-IN2 based on its in vitro dog TPO inhibition potency. Using human thyroid hormone turnover rates and TPO inhibitory potency, the human T4 and TSH concentrations upon MPO-IN1 treatment were predicted well. CONCLUSIONS: The model provides a scientific framework for the prediction of drug induced effects on plasma thyroid hormones concentrations in humans via TPO inhibition based on results obtained in in vitro and animal studies.

Has inhibition of Aß production adequately been tested as therapeutic approach in mild AD? A model-based meta-analysis of γ-secretase inhibitor data.

PURPOSE: To date, γ-secretase inhibition is the most frequently studied mechanism of reducing Aß in clinical trials with as yet no therapeutic success for AD patients, as measured by the slowing down of cognitive decline or an improvement in cognitive function. The aims of this investigation were to evaluate whether the amyloid hypothesis has been tested clinically, and to explore whether preclinical data are predictive of clinical Aß effects. METHODS: A model-based-meta analysis on Aß levels and drug exposure over time was performed on published and in-house (pre-)clinical data with γ-secretase inhibitors (GSIs; semagacestat, avagacestat, begacestat, PF-3074014, and MK0752). RESULTS: The clinical data available did not show any significant or robust reduction of CNS Aß over time at dose levels intended for AD patients. In contrast, these doses resulted in an average increase in plasma Aß levels over a 24-h interval. A general agreement between preclinical and clinical data was found and allowed for interspecies extrapolations. CONCLUSIONS: More substantially, CNS Aß-lowering drugs are needed to test whether inhibition of Aß production is efficacious in mild AD. Predictions based on preclinical data could assist in the selection of drug candidates and trial design.

Building an adaptive dose simulation framework to aid dose and schedule selection.

Establishing a dosing regimen that maximizes clinical benefit and minimizes adverse effects for novel therapeutics is a key objective for drug developers. Finding an optimal dose and schedule can be particularly challenging for compounds with a narrow therapeutic window such as in oncology. Modeling and simulation tools can be valuable to conduct in silico evaluations of various dosing scenarios with the goal to identify those that could minimize toxicities, avoid unscheduled dose interruptions, or minimize premature discontinuations, which all could limit the potential for therapeutic benefit. In this tutorial, we present a stepwise development of an adaptive dose simulation framework that can be used for dose optimization simulations. The tutorial first describes the general workflow, followed by a technical description with basic to advanced practical examples of its implementation in mrgsolve and is concluded with examples on how to use this in decision-making around dose and schedule optimization. The adaptive simulation framework is built with pharmacokinetic, pharmacodynamic (i.e., biomarkers, activity markers, target engagement markers, efficacy markers), and safety models that include evaluations of unexplained interindividual and intraindividual variability and covariate impact, which can be replaced and expanded (e.g., combination setting, comparator setting) with user-defined models. Subsequent adaptive simulations allow investigation of the impact of starting dose, dosing intervals, and event-driven (exposure or effect) dose modifications on any end point. The resulting simulation-derived insights can be used in quantitatively proposing dose and regimens that better balance benefit and adverse effects for further evaluation, aiding dose selection discussions, and designing dose modification recommendations, among others.

Model-based meta-analysis of non-small cell lung cancer with standard of care PD-1 inhibitors and chemotherapy for early development decision making.

Single-arm cohorts/trials are often used in early phase oncology programs to support preliminary clinical activity assessments for investigational products, administered alone or in combination with standard of care (SOC) agents. Benchmarking clinical activity of those combinations against other treatments, including SOC, requires indirect comparisons against external trials, which presents challenges including cross-study differences in trial populations/other factors. To facilitate such nonrandomized comparisons, we developed a comprehensive model-based meta-analysis (MBMA) framework to quantitatively adjust for factors related to efficacy in metastatic non-small cell lung cancer (mNSCLC). Data were derived from 15 published studies assessing key programmed cell death protein-1 (PD-1) inhibitors pembrolizumab (n = 8) and nivolumab (n = 7), representing current SOC in mNSCLC. In the first stage, a mixed-effects logistic regression model for overall response rate (ORR) was developed accounting for effects of various population covariates on ORR. The ORR model results indicated an odds ratio (OR) of 1.02 for squamous versus non-squamous histology and OR of 1.20 for PD-ligand 1 tumor proportion score (TPS) per every 10% increase of TPS level. Next, a nonparametric mixed-effects model for overall survival (OS) was developed with ORR/other clinical covariates as input. Subsequently, MBMA simulations of relevant hypothetical scenarios involving single-arm trial design predicted OS hazard ratios as a function of ORR with matched patient characteristics. Findings from this MBMA and derived parameter estimates can be generally applied by the reader as a framework for interpreting efficacy data from early phase trials to support ORR-based go/no-go decisions and futility rules, illustrated through examples in this report.

Decreased axonal transport rates in the Tg2576 APP transgenic mouse: improvement with the gamma-secretase inhibitor MRK-560 as detected by manganese-enhanced MRI.

Neuropil deposition of beta-amyloid (Aß) peptides is believed to be a key event in the neurodegenerative process of Alzheimer's disease (AD). An early and consistent clinical finding in AD is olfactory dysfunction with associated pathology. Interestingly, transgenic amyloid precursor protein (Tg2576) mice also show early amyloid pathology in olfactory regions. Moreover, a recent study indicates that axonal transport is compromised in the olfactory system of Tg2576 mice, as measured by manganese-enhanced magnetic resonance imaging (MEMRI). Here we tested whether the putative axonal transport deficit in the Tg2576 mouse model improves in response to a selective gamma-secretase inhibitor, N-[cis-4-[(4-chlorophenyl)-sulfonyl]-4-(2,5-difluorophenyl)cyclohexyl]-1,1,1-trifluoromethanesulfonamide (MRK-560). Tg2576 mice or wild-type (WT) littermates were treated daily with MRK-560 (30 µmol/kg) or vehicle for 4 (acute) or 29 days (chronic). The subsequent MEMRI analysis revealed a distinct axonal transport dysfunction in the Tg2576 mice compared with its littermate controls. Interestingly, the impairment of axonal transport could be fully reversed by chronic administration of MRK-560, in line with the significantly lowered levels of both soluble and insoluble forms of Aß found in the brain and olfactory bulbs (OBs) following treatment. However, no improvement of axonal transport was observed after acute treatment with MRK-560, where soluble but not insoluble forms of Aß were reduced in the brain and OBs. The present results show that axonal transport is impaired in Tg2576 mice compared with WT controls, as measured by MEMRI. Chronic treatment in vivo with a gamma-secretase inhibitor, MRK-560, significantly reduces soluble and insoluble forms of Aß, and fully reverses the axonal transport dysfunction.

Pharmacokinetic-pharmacodynamic modeling of the inhibitory effects of naproxen on the time-courses of inflammatory pain, fever, and the ex vivo synthesis of TXB2 and PGE2 in rats.

PURPOSE: To quantify and compare the time-course and potency of the analgesic and antipyretic effects of naproxen in conjunction with the inhibition of PGE(2) and TXB(2). METHODS: Analgesia was investigated in a rat model with carrageenan-induced arthritis using a gait analysis method. Antipyretics were studied in a yeast-induced fever model using telemetrically recorded body temperature. Inhibition of TXB(2) and PGE(2) synthesis was determined ex vivo. Pharmacokinetic profiles were obtained in satellite animals. Population PKPD modeling was used to analyze the data. RESULTS: The IC(50) values (95% CI) of naproxen for analgesia (27 (0-130) µM), antipyretics (40 (30-65) µM) and inhibition of PGE(2) (13 (6-45) µM) were in similar range, whereas inhibition of TXB(2) (5 (4-8) µM) was observed at lower concentrations. Variability in the behavioral measurement of analgesia was larger than for the other endpoints. The inhibition of fever by naproxen was followed by an increased rebound body temperature. CONCLUSION: Due to better sensitivity and similar drug-induced inhibition, the biomarker PGE(2) and the antipyretic effect would be suitable alternative endpoints to the analgesic effects for characterization and comparisons of potency and time-courses of drug candidates affecting the COX-2 pathway and to support human dose projections.

A Model-Informed Drug Discovery and Development Strategy for the Novel Glucose-Responsive Insulin MK-2640 Enabled Rapid Decision Making.

A model-informed drug discovery and development strategy played a key role in the novel glucose-responsive insulin MK-2640's early clinical development strategy and supported a novel clinical trial paradigm to assess glucose responsiveness. The development and application of in silico modeling approaches by leveraging substantial published clinical insulin pharmacokinetic-pharmacodynamic (PKPD) data and emerging preclinical and clinical data enabled rapid quantitative decision making. Learnings can be applied to define PKPD properties of novel insulins that could become therapeutically meaningful for diabetic patients.

Toward Progress in Quantitative Translational Medicine: A Call to Action.

Quantitative translational medicine (QTM) is envisioned as a multifaceted discipline that will galvanize the path from idea to medicine through quantitative translation across the discovery, development, regulatory, and utilization spectrum. Here, we summarize results of an American Society for Clinical Pharmacology and Therapeutics (ASCPT) survey on barriers relevant to the advancement of QTM and propose opportunities for its deployment. Importantly, we offer a call to action to break down these barriers through patient-centered stewardship, effective communication, cross-sector collaboration, and a modernized educational curriculum.

Model-Informed Drug Discovery and Development: Current Industry Good Practice and Regulatory Expectations and Future Perspectives.

Good practices around model-informed drug discovery and development (MID3) aim to improve the implementation, standardization, and acceptance of these approaches within drug development and regulatory review. A survey targeted to clinical pharmacology and pharmacometric colleagues across industry, the US Food and Drug Administration (FDA), and the European Medicines Agency (EMA) was conducted to understand current and future roles of MID3. The documented standards were generally affirmed as a "good match" to current industry practice and regulatory expectations, with some identified gaps that are discussed. All have seen at least a "modest" step forward in MID3 implementation associated with greater organizational awareness and share the expectation for a future wider use and impact. The priority within organizations was identified as a limitation with respect to the future of MID3. Finally, potential solutions, including a global overarching MID3 regulatory guideline, to facilitate greater acceptance by industry and regulatory decision makers are discussed.

Clinical Evaluation of MK-2640: An Insulin Analog With Glucose-Responsive Properties.

The goal of this investigation was to examine clinical translation of glucose responsiveness of MK-2640, which is a novel insulin saccharide conjugate that can bind the insulin receptor or mannose receptor C type 1 (MRC1), the latter dependent upon glucose concentration. In a rising dose study in 36 healthy adults under euglycemic clamp conditions, rising exposures revealed saturation of MK-2640 clearance, likely due to saturation of clearance by MRC1. Potency of MK-2640 was ~25-fold reduced relative to regular human insulin. In a randomized, 2-period crossover trial in 16 subjects with type 1 diabetes mellitus to evaluate glucose-responsiveness of i.v. administered MK-2640, we were unable to demonstrate a glucose-dependent change in MK-2640 clearance, although a significant glucose-dependent augmentation of glucose infusion rate was observed. These pharmacokinetic (PK) and pharmacodynamic (PD) data provide crucial insights into next steps for developing an insulin saccharide conjugate as a clinically effective glucose-responsive insulin analog.

Getting Innovative Therapies Faster to Patients at the Right Dose: Impact of Quantitative Pharmacology Towards First Registration and Expanding Therapeutic Use.

Quantitative pharmacology (QP) applications in translational medicine, drug-development, and therapeutic use were crowd-sourced by the ASCPT Impact and Influence initiative. Highlighted QP case studies demonstrated faster access to innovative therapies for patients through 1) rational dose selection for pivotal trials; 2) reduced trial-burden for vulnerable populations; or 3) simplified posology. Critical success factors were proactive stakeholder engagement, alignment on the value of model-informed approaches, and utilizing foundational clinical pharmacology understanding of the therapy.