Model-informed Drug Development Research Articles

#362 Dosing low, winning high: unlocking the promise of endothelin antagonism for the treatment of CKD in the zibotentan/dapagliflozin ZENITH trials

Abstract Background and Aims The Phase 2b ZENITH-CKD trial demonstrated that combined treatment with the endothelin A receptor (ETAR) antagonist zibotentan and the sodium-glucose co-transporter-2 inhibitor (SGLT2i) dapagliflozin yields a robust and significant reduction in albuminuria in individuals with chronic kidney disease (CKD), compared to dapagliflozin treatment alone [1]. This is hypothesized to translate into a significant reduction of kidney function decline and may further confer cardiometabolic benefits including reducing blood pressure, LDL cholesterol and (in type 2 diabetes) HbA1c in the ongoing Phase 3 ZENITH High Proteinuria trial. The usage of ETAR antagonists has until now been limited by the class effect of fluid retention, which is associated with an increased risk of heart failure in the at-risk CKD population. Zibotentan has previously been investigated at 10 mg per day in Phase 3 for the treatment of prostate cancer. Method Model-Informed Drug Development (MIDD) was integrated with clinical pharmacology data to develop the dose strategy for both ZENITH trials. A low-dose strategy was selected considering the increased plasma exposure of zibotentan in kidney impairment. The Phase 2b dose steps were selected based on an exposure response model for the efficacy marker urinary albumin to creatinine ratio (uACR) and were supported using clinical trial simulations. The dose strategy for Phase 3 was defined based on a combined analysis of ZENITH-CKD results for both efficacy and tolerability: efficacy (uACR reduction on top of dapagliflozin) was analysed with a Dose-Response Mixed Model for Repeated Measures, and tolerability (fluid retention events, defined as a combination of specified body weight and BNP increase) was evaluated with Kaplan Meier analyses. Results Zibotentan doses of 0.25 mg and 1.5 mg (in a first, discontinued study part also 5 mg), in combination with dapagliflozin 10 mg, were selected for ZENITH-CKD. Clinical trial simulations indicated that inclusion of the low dose of 0.25 mg was crucial to characterise the dose-response relationship. Dose-uACR response modelling of ZENITH-CKD data predicted a zibotentan dose of 0.75 mg to result in a mean uACR reduction of around 25% in addition to the reduction with dapagliflozin. A post-hoc regression analysis of uACR mean changes from baseline, stratified by dose, showed a trend towards lower uACR reduction with higher eGFR levels. Kaplan-Meier analyses of the fluid retention risk in different eGFR subgroups indicated that the fluid retention risk increased with increasing zibotentan dose for eGFR levels <45 mL/min/1.73 m2, while for higher eGFR levels, no differences were observed between treatments with dapagliflozin alone and added zibotentan 0.25 mg or 1.5 mg. Therefore, a kidney-function based zibotentan dose strategy was selected for the ZENITH High Proteinuria trial: Combined zibotentan/dapagliflozin is being compared against dapagliflozin alone; within the combination treatment arm, 0.25 mg zibotentan is selected for participants with baseline eGFR levels <45 mL/min/1.73 m2, and 0.75 mg is selected for higher eGFR level, with a tolerance eGFR window for participants whose eGFR declines below this cut-off, and with the possibility to reduce the dose to 0.25 mg in case of tolerability findings. Conclusion The favourable benefit/risk profile that was demonstrated for combined zibotentan/dapagliflozin in ZENITH-CKD was enabled by the zibotentan low-dose strategy. The wide dose ranging and MIDD further enabled selection of a kidney-function based zibotentan dosing strategy for ZENITH High Proteinuria which is designed to further balance benefit/risk. The low-dose zibotentan strategy and the combination with dapagliflozin opened for the first time a therapeutic window for ETAR antagonists, with the potential to redefine their value for the treatment of CKD. This work was sponsored by AstraZeneca.

A Model-Informed Drug Development Approach to Design a Phase 3 Trial of Teverelix Drug Product in Advanced Prostate Cancer Patients with Increased Cardiovascular Risk.

Teverelix drug product (DP) is a parenteral gonadotropin-releasing hormone (GnRH) antagonist that has been successfully tested in phase 2 trials for hormone-sensitive advanced prostate cancer (APC) and benign prostatic hyperplasia (BPH). In previous APC trials, teverelix DP was administered as intramuscular (IM) and subcutaneous (SC) injections, using a loading dose and (in a single trial) a maintenance dose. Our objective was to derive an optimal dosing regimen for phase 3 clinical development, using a pharmacometrics modeling approach. Data from 9 phase 2 studies (229 patients) was utilized to develop a population pharmacokinetic (PK) model that described the concentration profile accommodating both IM and SC routes of administration. A 2-compartment model with sequential first-order absorption (slow and fast) and lag times best described the PK profiles of teverelix following SC and IM administration. An indirect response model with inhibition of production rate was fit to describe testosterone (T) concentrations based on physiological relevance. The final population PK-pharmacodynamic model was used to conduct simulations of various candidate dosing regimens to select the optimal dosing regimen to achieve clinical castration (T<0.5ng/mL by day 28) and to sustain clinical castration for 26 weeks. Model simulation showed that a loading dose of 360mg SC and 180mg IM with a maintenance dose of 360mg SC 6-weekly (Q6W) starting at day 28 can achieve a ≥95% castration rate up to 52 weeks. This dose regimen was selected for phase 3 clinical development, which includes cardiovascular safety assessment in comparison to a GnRH agonist.

Model-Informed Clinical Development of Once-Every-6-Month Injection of Paliperidone Palmitate in Patients with Schizophrenia: A Pharmacometric Bridging Approach (Part I).

A model-informed drug development (MIDD) approach was implemented for paliperidone palmitate (PP) 6-month (PP6M) clinical development, using pharmacokinetics and pharmacokinetic/pharmacodynamic model-based simulations. PP6M pharmacokinetics were simulated by extending the PP 3-month (PP3M) pharmacokinetic model to account for increased injection volume, and hence dose. Contribution of the MIDD approach to the design of the pivotal PP6M phase-3 study (PP6M/PP3M noninferiority study, NCT03345342) investigating schizophrenia relapse rates was twofold: (1) PP6M dose selection, and (2) hypothesis generation that lower trough concentrations (Ctrough) associated with PP6M, relative to PP3M, were not associated with lower efficacy, which was to be evaluated in the phase-3 study. Moreover, accompanied by an intense sampling scheme to adequately characterize paliperidone pharmacokinetics and to elucidate the potential relationship between concentration and safety/efficacy, the bridging strategy eliminated the need for additional phase-1/phase-2 clinical studies. Using a MIDD bridging strategy, PP6M doses were selected that, compared with PP3M, were expected to have a similar range of exposures and a noninferior relapse rate and safety profile. Clinical data from PP6M/PP3M noninferiority study confirmed that PP6M, compared with PP3M, had a similar range of exposures (T'jollyn etal. in Eur J Drug Metab Pharmacokinet 2024), as well as a noninferior relapse rate and safety profile (this manuscript). Consistency of the MIDD approach with observed clinical outcomes confirmed the hypothesis that lower Ctrough did not lead to increased relapse rates at the doses administered. Although higher paliperidone peak concentrations are achieved with corresponding doses of PP6M relative to PP3M in the phase-3 clinical study, types and incidences of treatment-related adverse events were comparable between PP6M and PP3M groups and no new safety concerns emerged for PP6M (Najarian etal. in Int J Neuropsychopharmacol 25(3):238-251, 2022).

A Minimal PBPK/PD Model with Expansion-Enhanced Target-Mediated Drug Disposition to Support a First-in-Human Clinical Study Design for a FLT3L-Fc Molecule.

FLT3L-Fc is a half-life extended, effectorless Fc-fusion of the native human FLT3-ligand. In cynomolgus monkeys, treatment with FLT3L-Fc leads to a complex pharmacokinetic/pharmacodynamic (PK/PD) relationship, with observed nonlinear PK and expansion of different immune cell types across different dose levels. A minimal physiologically based PK/PD model with expansion-enhanced target-mediated drug disposition (TMDD) was developed to integrate the molecule's mechanism of action, as well as the complex preclinical and clinical PK/PD data, to support the preclinical-to-clinical translation of FLT3L-Fc. In addition to the preclinical PK data of FLT3L-Fc in cynomolgus monkeys, clinical PK and PD data from other FLT3-agonist molecules (GS-3583 and CDX-301) were used to inform the model and project the expansion profiles of conventional DC1s (cDC1s) and total DCs in peripheral blood. This work constitutes an essential part of our model-informed drug development (MIDD) strategy for clinical development of FLT3L-Fc by projecting PK/PD in healthy volunteers, determining the first-in-human (FIH) dose, and informing the efficacious dose in clinical settings. Model-generated results were incorporated in regulatory filings to support the rationale for the FIH dose selection.

In-Depth Analysis of the Selection of PBPK Modeling Tools: Bibliometric and Social Network Analysis of the Open Systems Pharmacology Community.

Since the Open Source Initiative laid the foundation for the open source software environment in 1998, the popularity of free and open source software has been steadily increasing. Model-informed drug discovery and development (MID3), a key component of pharmaceutical research and development, heavily makes use of computational models which can be developed using various software including the Open Systems Pharmacology (OSP) software (PK-Sim/MoBi), a free and open source software tool for physiologically based pharmacokinetic (PBPK) modeling. In this study, we aimed to investigate the impact, application areas, and reach of the OSP software as well as the relationships and collaboration patterns between organizations having published OSP-related articles between 2017 and 2023. Therefore, we conducted a bibliometric analysis of OSP-related publications and a social network analysis of the organizations with which authors of OSP-related publications were affiliated. On several levels, we found evidence for a significant growth in the size of the OSP community as well as its visibility in the MID3 community since OSP's establishment in 2017. Specifically, the annual publication rate of PubMed-indexed PBPK-related articles using the OSP software outpaced that of PBPK-related articles using any software. Our bibliometric analysis and network analysis demonstrated that the expansion of the OSP community was predominantly driven by new authors and organizations without prior connections to the community involving the generation of research clusters de novo and an overall diversification of the network. These findings suggest an ongoing evolution of the OSP community toward a more segmented, diverse, and inclusive network.

Dose Optimization Informed by PBPK Modeling: State-of-the Art and Future.

Model-informed drug development (MIDD) is a powerful quantitative approach that plays an integral role in drug development and regulatory review. While applied throughout the life cycle of the development of new drugs, a key application of MIDD is to inform clinical trial design including dose selection and optimization. To date, physiologically-based pharmacokinetic (PBPK) modeling, an established component of the MIDD toolkit, has mainly been used for assessment of drug-drug interactions (DDIs) and consequential dose adjustments in regulatory submissions. As a result of recent scientific advances and growing confidence in the utility of the approach, PBPK models are being increasingly applied to provide dose recommendations for subjects with differing ages, genetics, and disease states. In this review, we present our perspective on the current landscape of regulatory acceptance of PBPK applications supported by relevant case studies. We also discuss the recent progress and future challenges associated with expanding the utility of PBPK models into emerging areas for regulatory decision making, especially dose optimization in highly vulnerable and understudied populations and facilitating diversity in clinical trials.

Quantitative Assessment of Drug Efficacy and Emergence of Resistance in Patients with Metastatic Renal Cell Carcinoma Using a Longitudinal Exposure-Tumor Growth Inhibition Model: Apitolisib (Dual PI3K/mTORC1/2 Inhibitor) Versus Everolimus (mTORC1 Inhibitor).

Cancer remains a significant global health challenge, and despite remarkable advancements in therapeutic strategies, poor tolerability of drugs (causing dose reduction/interruptions) and/or the emergence of drug resistance are major obstacles to successful treatment outcomes. Metastatic renal cell carcinoma (mRCC) accounts for 2% of global cancer diagnoses and deaths. Despite the initial success of targeted therapies in mRCC, challenges remain to overcome drug resistance that limits the long-term efficacy of these treatments. Our analysis aim was to develop a semi-mechanistic longitudinal exposure-tumor growth inhibition model for patients with mRCC to characterize and compare everolimus (mTORC1) and apitolisib's (dual PI3K/mTORC1/2) ability to inhibit tumor growth, and quantitate each drug's efficacy decay caused by emergence of tumor resistance over time. Model-estimated on-treatment tumor growth rate constant was 1.7-fold higher for apitolisib compared to everolimus. Estimated half-life for loss of treatment effect over time for everolimus was 16.1 weeks compared to 7.72 weeks for apitolisib, suggesting a faster rate of tumor re-growth for apitolisib patients likely due to the emergence of resistance. Goodness-of-fit plots including visual predictive check indicated a good model fit and the model was able to capture individual tumor size-time profiles. Based on our knowledge, this is the first clinical report to quantitatively assess everolimus (mTORC1) and apitolisib (PI3K/mTORC1/2) efficacy decay in patients with mRCC. These results highlight the difference in overall efficacy of 2 drugs due to the quantified efficacy decay caused by emergence of resistance, and emphasize the importance of model-informed drug development for targeted cancer therapy.

Dosing optimization of rituximab for primary membranous nephropathy by population pharmacokinetic and pharmacodynamic study.

Primary membranous nephropathy (PMN) is the most common cause for adult nephrotic syndrome. Rituximab has demonstrated promising clinical efficacy by random controlled trials and the off-label use is widely adopted in PMN. However, the standard dosage is borrowed from B cell lymphoma treatment with far more antigens and is oversaturated for PMN treatment, accompanied with additional safety risk and unnecessary medical cost. More than 15% serious adverse events were observed under standard dosage and low dose therapies were explored recently. Dose optimization by clinical trials is extremely time- and cost-consuming and can be significantly accelerated with the aid of model-informed drug development. Here, we aim to establish the first population pharmacokinetic and pharmacodynamic (PPK/PD) model for rituximab in PMN to guide its dosage optimization. Rituximab pharmacokinetic and pharmacodynamic data from 41 PMN patients in a retrospective study under a newly proposed monthly mini-dose were used to construct quantitative dose-exposure-response relationship via mechanistic target-mediated drug disposition (TMDD) model followed by regression between the reduction of anti-PLA2R titer and time after the treatment. The final model, validated by goodness-of-fit plots, visual predictive checks and bootstrap, was used to recommend the optimized dosing regimen by simulations. The model was well validated for PK/PD prediction. The systemic clearance and half-life are 0.54L/h and 14.7 days, respectively. Simulation of a novel regimen (6 monthly doses of 100mg) indicated the comparable ability and superior duration time of CD20+ B cell depletion compared with standard dosage, while the cumulative dosage and safety risk was significantly decreased. We established the first PPK/PD model and provide evidence to support the dosage optimization based on monthly mini-dose. Our study can also efficiently accelerate dosage optimization of novel anti-CD20 antibodies in PMN and other indications.

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.

Considerations for Industry-Preparing for the FDA Model-Informed Drug Development (MIDD) Paired Meeting Program.

A recent industry perspective published in this journal describes the benefits received by drug companies from participation in the MIDD Pilot Program. Along with the primary objectives of supporting good decision-making in drug development, there were substantial savings in time and development costs. Furthermore, many sponsors reported qualitative benefits such as new learnings and clarity on MIDD strategies and methodology that could be applied to other development programs. Based on the success of the Pilot Program, the FDA recently announced the continuation of the MIDD Paired Meeting Program as part of the Prescription Drug User Fee Act (PDUFA VII). In this report, we describe the collective experiences of industry participants in the MIDD Program to date, including all aspects of the process from meeting request submission to follow-up actions. The purpose is to provide future participants with information to optimize the value of the MIDD Program.

Realizing the promise of Project Optimus: Challenges and emerging opportunities for dose optimization in oncology drug development.

Project Optimus is a US Food and Drug Administration Oncology Center of Excellence initiative aimed at reforming the dose selection and optimization paradigm in oncology drug development. This project seeks to bring together pharmaceutical companies, international regulatory agencies, academic institutions, patient advocates, and other stakeholders. Although there is much promise in this initiative, there are several challenges that need to be addressed, including multidimensionality of the dose optimization problem in oncology, the heterogeneity of cancer and patients, importance of evaluating long-term tolerability beyond dose-limiting toxicities, and the lack of reliable biomarkers for long-term efficacy. Through the lens of Totality of Evidence and with the mindset of model-informed drug development, we offer insights into dose optimization by building a quantitative knowledge base integrating diverse sources of data and leveraging quantitative modeling tools to build evidence for drug dosage considering exposure, disease biology, efficacy, toxicity, and patient factors. We believe that rational dose optimization can be achieved in oncology drug development, improving patient outcomes by maximizing therapeutic benefit while minimizing toxicity.

Physiologically based pharmacokinetic modeling of imatinib and N-desmethyl imatinib for drug-drug interaction predictions.

The first-generation tyrosine kinase inhibitor imatinib has revolutionized the development of targeted cancer therapy and remains among the frontline treatments, for example, against chronic myeloid leukemia. As a substrate of cytochrome P450 (CYP) 2C8, CYP3A4, and various transporters, imatinib is highly susceptible to drug-drug interactions (DDIs) when co-administered with corresponding perpetrator drugs. Additionally, imatinib and its main metabolite N-desmethyl imatinib (NDMI) act as inhibitors of CYP2C8, CYP2D6, and CYP3A4 affecting their own metabolism as well as the exposure of co-medications. This work presents the development of a parent-metabolite whole-body physiologically based pharmacokinetic (PBPK) model for imatinib and NDMI used for the investigation and prediction of different DDI scenarios centered around imatinib as both a victim and perpetrator drug. Model development was performed in PK-Sim® using a total of 60 plasma concentration-time profiles of imatinib and NDMI in healthy subjects and cancer patients. Metabolism of both compounds was integrated via CYP2C8 and CYP3A4, with imatinib additionally transported via P-glycoprotein. The subsequently developed DDI network demonstrated good predictive performance. DDIs involving imatinib and NDMI were simulated with perpetrator drugs rifampicin, ketoconazole, and gemfibrozil as well as victim drugs simvastatin and metoprolol. Overall, 12/12 predicted DDI area under the curve determined between first and last plasma concentration measurements (AUClast ) ratios and 12/12 predicted DDI maximum plasma concentration (Cmax ) ratios were within twofold of the respective observed ratios. Potential applications of the final model include model-informed drug development or the support of model-informed precision dosing.

Pediatric Cancer Drug Development: Leveraging Insights in Cancer Biology and the Evolving Regulatory Landscape to Address Challenges and Guide Further Progress.

The discovery and development of anticancer drugs for pediatric patients have historically languished when compared to both past and recent activity in drug development for adult patients, notably the dramatic spike of targeted and immune-oncology therapies. The reasons for this difference are multifactorial. Recent changes in the regulatory landscape surrounding pediatric cancer drug development and the understanding that some pediatric cancers are driven by genetic perturbations that also drive disparate adult cancers afford new opportunities. The unique cancer-initiating events and dependencies of many pediatric cancers, however, require additional pediatric-specific strategies. Research efforts to unravel the underlying biology of pediatric cancers, innovative clinical trial designs, model-informed drug development, extrapolation from adult data, addressing the unique considerations in pediatric patients, and use of pediatric appropriate formulations, should all be considered for efficient development and dosage optimization of anticancer drugs for pediatric patients.

Advancing precision medicine therapeutics for Parkinson’s utilizing a shared quantitative systems pharmacology model and framework

A rich pipeline of therapeutic candidates is advancing for Parkinson’s disease, many of which are targeting the underlying pathophysiology of disease. Emerging evidence grounded in novel genetics and biomarker discoveries is illuminating the true promise of precision medicine-based therapeutic strategies for PD. There has been a growing effort to investigate disease-modifying therapies by designing clinical trials for genetic forms of PD - providing a clearer link to underlying pathophysiology. Leading candidate genes based on human genetic findings that are under active investigation in an array of basic and translational models include SNCA, LRRK2, and GBA. Broad investigations across mechanistic models show that these genes signal through common molecular pathways, namely, autosomal lysosomal pathways, inflammation and mitochondrial function. Therapeutic clinical trials to date based on genetically defined targets have not yet achieved approvals; however, much is to be learned from such pioneering trials. Fundamental principles of drug development that include proof of pharmacology in target tissue are critical to have confidence in advancing such precision-based therapies. There is a clear need for downstream biomarkers of leading candidate therapies to demonstrate proof of mechanism. The current regulatory landscape is poised and primed to support translational modeling strategies for the effective advancement of PD disease-modifying therapeutic candidates. A convergence of rich complex data that is available, the regulatory framework of model informed drug development (MIDD), and the new biological integrated staging frameworks when combined are collectively setting the stage for advancing new approaches in PD to accelerate progress. This perspective review highlights the potential of quantitative systems pharmacology (QSP) modeling in contributing to the field and hastening the pace of progress in advancing collaborative approaches for urgently needed PD disease-modifying treatments.

Clinical Pharmacology Perspective on Development of Adeno-Associated Virus Vector-Based Retina Gene Therapy.

Adeno-associated virus (AAV) vector-based gene therapy is an innovative modality being increasingly investigated to treat diseases by modifying or replacing defective genes or expressing therapeutic entities. With its unique anatomic and physiological characteristics, the eye constitutes a very attractive target for gene therapy. Specifically, the ocular space is easily accessible and is generally considered "immune-privileged" with a low risk of systemic side effects following local drug administration. As retina cells have limited cellular turnover, a one-time gene delivery has the potential to provide long-term transgene expression. Despite the initial success with voretigene neparvovec (Luxturna), the first approved retina gene therapy, there are still challenges to be overcome for successful clinical development of these products and scientific questions to be answered. The current review paper aims to integrate published experience learned thus far for AAV-based retina gene therapy related to preclinical to clinical translation; first-in-human dose selection; relevant bioanalytical assays and strategies; clinical development considerations including trial design, biodistribution and vector shedding, immunogenicity, transgene expression, and pediatric populations; opportunities for model-informed drug development; and regulatory perspectives. The information presented herein is intended to serve as a guide to inform the clinical development strategy for retina gene therapy with a focus on clinical pharmacology.

The US Food and Drug Administration's Model-Informed Drug Development Meeting Program: From Pilot to Pathway.

The US Food and Drug Administration's Model-Informed Drug Development Meeting Program: From Pilot to Pathway.

An industry perspective on current QSP trends in drug development.

2023 marks the 10th anniversary of Natpara's submission to the US FDA, which led to the first recorded regulatory interaction where a decision was supported by Quantitative and Systems Pharmacology (QSP) simulations. It had taken about 5 years for the timid QSP discipline to emerge as an effective Model-Informed Drug Development (MIDD) tool with visible impact in the pharmaceutical industry. Since then, the presence of QSP in the regulatory environment has continued to increase, to the point that the Agency reported 60 QSP submissions in 2020 alone, representing ~ 4% of their annual IND submissions [1]. What sort of industry mindset has enabled QSP to reach this level of success? How does QSP fit within the MIDD paradigm? Does QSP mean the same to Discovery and to Clinical Development projects? How do 'platforms' compare to 'fit-for-purpose' QSP models in an industrial setting? Can QSP and empirical Pharmacokinetic-Pharmacodynamic (PKPD) modelling be complementary? What level of validation is required to inform drug development decisions? This article reflects on all these questions, in particular addressing those audiences with limited line-of-sight into the drug industry decision-making machinery.

Abstract IA012: Targeting AXL in platinum-resistant ovarian cancer: from the lab bench to Phase 3 trial

Abstract IA012: Targeting AXL in platinum-resistant ovarian cancer: from the lab bench to Phase 3 trial

Application of Model-Informed Drug Development in Dose Selection and Optimization for siRNA Therapies.

The application of model-informed drug development (MIDD) has revolutionized drug development and regulatory decision making, transforming the process into one that is more efficient, effective, and patient centered. A critical application of MIDD is to facilitate dose selection and optimization, which play a pivotal role in improving efficacy, safety, and tolerability profiles of a candidate drug. With the surge of interest in small interfering RNA (siRNA) drugs as a promising class of therapeutics, their applications in various disease areas have been extensively studied preclinically. However, dosing selection and optimization experience for siRNA in humans is limited. Unique challenges exist for the dose evaluation of siRNA due to the temporal discordance between pharmacokinetic and pharmacodynamic profiles, as well as limited available clinical experience and considerable interindividual variability. This review highlights the pivotal role of MIDD in facilitating dose selection and optimization for siRNA therapeutics. Based on past experiences with approved siRNA products, MIDD has demonstrated its ability to aid in dose selection for clinical trials and enabling optimal dosing for the general patient population. In addition, MIDD presents an opportunity for dose individualization based on patient characteristics, enhancing the precision and effectiveness of siRNA therapeutics. In conclusion, the integration of MIDD offers substantial advantages in navigating the complex challenges of dose selection and optimization in siRNA drug development, which in turn accelerates the development process, supports regulatory decision making, and ultimately improves the clinical outcomes of siRNA-based therapies, fostering advancements in precision medicine across a diverse range of diseases.

Moving Beyond Boundaries: Utilization of Longitudinal Exposure-Response Model for Bounded Outcome Score to Inform Decision Making in the Accelerated Drug Development Paradigm.

As drug development scientists strive to accelerate availability of therapies for patients, model-informed drug development (MIDD) plays an important role in contextualizing existing information and facilitating decision making. This paper describes an example of MIDD, where modeling and simulation informed decision making in the circumstance of a combined phase 2b and single pivotal study for ritlecitinib (JAK3/TEC family kinases inhibitor). Longitudinal exposure-response (ER) modeling was conducted to describe ritlecitinib efficacy in alopecia areata patients. The Severity of Alopecia Tool (SALT) score (a continuous bounded outcome [CBO] score [0-100]) was used as the efficacy response. The average concentration during the time interval between two adjacent SALT scores was used as the exposure metric driving efficacy. The developed model well described the longitudinal SALT profile of ritlecitinib as well as the frequency of boundary data. The CBO model indicated tested doses in the phase 2b/3 clinical trial are in the ascending region of ER and contextualized a loading dose effect that impacted onset of efficacy without long-term benefit. It also identified disease severity as the only covariate impacting efficacy. The model-based simulation further informed impact of treatment interruption on the loss of efficacy in the absence of a dedicated treatment withdrawal study. Results indicated temporary treatment interruption ≤ 6 weeks is not expected to result in significant loss of efficacy. The CBO modeling approach and simulation supported the single pivotal trial strategy and guided dose selection in the accelerated drug development program of ritlecitinib, which can be applied to many indications where efficacy is measured on a bounded scale.