Model-informed Drug Development Research Articles

Flat dose regimen of toripalimab based on model-informed drug development approach.

Introduction: Flat dosing regimen has recently been approved for programmed death receptor-1 (PD-1) inhibitors including toripalimab, nivolumab and pembrolizumab. The objective of this study is to provide pharmacological evidence for a flat dosing regimen of toripalimab by assessing the efficacy and safety profile of a 240mg Q3W flat dose relative to the currently approved 3mg/kg Q2W. Methods: A population pharmacokinetic (PopPK) model was established based on 1,014 evaluable patients in 13 clinical studies. The exposure-objective response rate (ORR, n = 234) and exposure-safety (n = 152) analyses were performed by logistic regression. Three safety endpoints including grade ≥ 3 adverse events (AEs), treatment-related grade ≥ 3 AEs, and AEs leading to study drug discontinuation were evaluated. Progression-free survival (PFS, n = 234) was evaluated using a Cox proportional hazard model with the Kaplan-Meier survival curve. Results: The PK profiles of toripalimab are best described by a two-compartment model with time-varying clearance characterized by a sigmoidal maximum effect (Emax) function. Simulations for the first dose and steady-state exposures for the 240mg Q3W dosing regimen were comparable to those for the 3mg/kg Q2W dosing regimen with 95% exposure coverage ranging from 88% to 96%. The exposure-safety analysis showed that the probability of an adverse event occurring did not increase with increases in toripalimab exposure. A flat exposure-response relationship for ORR was identified. The Kaplan-Meier survival curve showed that exposure was a predictor for PFS; however, no difference in treatment benefit was demonstrated across exposure quantiles using a Cox proportional hazard model. Discussion: This study revealed that toripalimab exposure of 240mg Q3W dosing regimen was comparable to 3mg/kg Q2W dosing regimen. The safety and efficacy E-R results of 240mg Q3W is flat. Hence, the 240mg Q3W dosing regimen is determined to be a preferred therapeutic dosage for toripalimab due to the convenience of flat dose.

Quantitative systems modeling approaches towards model-informed drug development: Perspective through case studies

Quantitative systems pharmacology (QSP) modeling has become an increasingly popular approach impacting our understanding of disease mechanisms and helping predict patients’ treatment responses to facilitate study design or development go/no-go decisions. In this paper, we highlight the notable contributions and opportunities that QSP approaches are to offer during the drug development process by sharing three examples that have facilitated internal decisions. The barriers to successful applications and the factors that facilitate the success of the modeling approach is discussed.

Using mechanistic models to support development of complex generic drug products: European Medicines Agency perspective.

Model-informed drug development (MIDD) approaches receive wide regulatory acceptance in the European Medicines Agency (EMA) to support new drug development. For generic drugs, the European regulators have not reached a common position on how to use these methods. This commentary expands on the existing EMA regulatory framework for bioequivalence and physiological based pharmacokinetic (PBPK) modeling to propose conditions where mechanistic models could support or potentially waive clinical bioequivalence (BE)/bioavailability (BA) studies.

CYTOCON DB: A versatile database of human cell and molecule concentrations for accelerating model development

<scp>CYTOCON DB</scp>: A versatile database of human cell and molecule concentrations for accelerating model development

Model-Informed drug development of gastroretentive release systems for sildenafil citrate

Gastroretentive drug delivery systems (GRDDS) are modified-release dosage forms designed to prolong their residence time in the upper gastrointestinal tract, where some drugs are preferentially absorbed, and increase the drug bioavailability. This work aimed the development of a novel GRDDS containing 60 mg of sildenafil citrate, and the evaluation of the feasibility of the proposed formulation for use in the treatment of pulmonary arterial hypertension (PAH), for once a day administration, by using in silico pharmacokinetic (PK) modeling and simulations using GastroPlusTM. The Model-Informed Drug Development (MIDD) approach was used in formulation design and pharmacokinetic exposure prospecting. A 22 factorial design with a central point was used for optimization of the formulation, which was produced by direct compression and characterized by some tests, including buoyancy test, assay, impurities, and in vitro dissolution. A compartmental PK model was built using the GatroPlusTM software for virtual bioequivalence of the proposed formulations in comparison with the defined target release profile provided by an immediate release (IR) tablet formulation containing 20 mg of sildenafil administered three times a day (TID). The results of the factorial design showed a direct correlation between the dissolution rate and the amount of hydroxypropyl methyl cellulose (HPMC) in the formulations. By comparing the PK parameters predicted by the virtual bioequivalence, the formulations F1, F2, F3 and F5 failed on bioequivalence. The F4 showed bioequivalence to the reference and was considered the viable formulation to substitute the IR. Thus, GRDDS could be a promising alternative for controlling the release of drugs with a pH-dependent solubility and narrow absorption window, specifically in the gastric environment, and an interesting way to reduce dose frequency and increase the drug bioavailability. The MIDD approach increases the level of information about the pharmaceutical product and guide the drug development for more assertive ways.

Convolution-based approach for modeling the paliperidone extended release and Long-Acting Injectable (LAI) PK of once-, and three-monthly products administration and for optimizing the development of new LAI products

The aim of this paper was to develop a convolution-based modeling approach for describing the paliperidone PK resulting from the administration of extended-release once-a-day oral dose, and once- and three monthly long-acting injectable products and to compare the performances of this approach to the traditional modeling strategy. The results of the analyses indicated that the traditional and convolution-based models showed comparable performances in the characterization of the paliperidone PK. However, the convolution-based approach showed several appealing features that justify the choice of this modeling as a preferred tool for modeling Long Acting Injectable (LAI) products and for deploying an effective model-informed drug development process. In particular, the convolution-based modeling can (a) facilitate the development of in vitro/in vivo correlation, (b) be used to identify formulations with optimal in vivo release properties, and (c) be used for optimizing the clinical benefit of a treatment by supporting the implementation of integrated models connecting in vitro and in vivo drug release, in vivo drug release to PK, and PK to PD and biomarker endpoints. A case study was presented to illustrate the benefits and the flexibility of the convolution-based modeling outcomes. The model was used to evaluate the in vivo drug release properties associated with a hypothetical once-a-year administration of a LAI product with the assumption that the expected paliperidone exposure during a 3-year treatment overlays the exposure expected after repeated administrations of a 3-month LAI product.

Disease trajectory of SLE clinical endpoints and covariates affecting disease severity and probability of response: Analysis of pooled patient-level placebo (Standard-of-Care) data to enable model-informed drug development.

Systemic lupus erythematosus (SLE) is an autoimmune disease affecting multiple organ systems. Many investigational agents have failed or shown only modest effects when added to standard of care (SoC) therapy in placebo-controlled trials, and only two therapies have been approved for SLE in the last 60 years. Clinical trial outcomes have shown discordance in drug effects between clinical endpoints. Herein, we characterized longitudinal disease activity in the SLE population and the sources of variability by developing a latent disease trajectory model for SLE component endpoints (Systemic Lupus Erythematosus Disease Activity Index [SLEDAI], Physician's Global Assessment [PGA], British Isles Lupus Assessment Group Index [BILAG]) and composite endpoints (Systemic Lupus Erythematosus Responder Index [SRI], BILAG-based Composite Lupus Assessment [BICLA], and Lupus Low Disease Activity State [LLDAS]) using patient-level historical SoC data from nine phase II and III studies. Across all endpoints, in predictions up to 52 weeks from the final disease trajectory model, the following baseline covariates were associated with a greater decrease in SLE disease activity and higher response to placebo + SoC: Hispanic ethnicity from Central/South America, absence of hypocomplementemia, recent SLE diagnosis, and high baseline disease activity score using SLEDAI and BILAG separately. No discernible differences were observed in the trajectory of response to placebo + SoC across different SoC medications (antimalarial and immunosuppressant such as mycophenolate, methotrexate, and azathioprine). Across all endpoints, disease trajectory showed no difference in Asian versus non-Asian patients, supporting Asia-inclusive global SLE drug development. These results describe the first population approach to support a model-informed drug development framework in SLE.

Learn-confirm in model-informed drug development: Assessing an immunogenicity quantitative systems pharmacology platform.

Immunogenicity against therapeutic proteins frequently causes attrition owing to its potential impact on pharmacokinetics, pharmacodynamics, efficacy, and safety. Predicting immunogenicity is complex because of its multifactorial drivers, including compound properties, subject characteristics, and treatment parameters. To integrate these, the Immunogenicity Simulator was developed using published, predominantly late-stage trial data from 15 therapeutic proteins. This single-blinded evaluation with subject-level data from 10 further monoclonals assesses the Immunogenicity Simulator's credibility for application during the drug development process.

Algorithms and methodological challenges in the development and application of quantitative systems pharmacology models: a case study in type 2 diabetes

Abstract Quantitative systems pharmacology (QSP) is a relatively new modelling discipline, formed within the ever-growing domain of model-informed drug development and actively evolving throughout the last decade. This modelling technique is based on the systems analysis and is used to get a quantitative rather than qualitative understanding of systems dynamics and explore the mechanisms of action of a drug. However, there is no well-defined methodology for the QSP model development, which significantly complicates the practical application of these models. In the current work, we overview the existing mathematical models of antidiabetic therapies and propose a modelling method, which overcomes common limitations and is able to produce a physiologically based mechanistic model describing gliflozin action in type 2 diabetes mellitus. From the practical standpoint, sensitivity analysis preformed in this work helped to reveal subpopulation of patients with better response to gliflozin therapy.

Population Pharmacokinetics of Oral Migalastat in Adolescents and Adults With and Without Renal Impairment.

Migalastat is approved for the treatment of Fabry disease (FD) with amenable variants. Objectives were to characterize effects of estimated glomerular filtration rate (eGFR) on oral clearance (CL), predict doses in mild to moderate renal impairment and in pediatric patients with FD, and to improve designs of FD studies. A 2-compartment model was fit to data from 260 subjects with/without FD and iteratively refined with evolving data. FD, eGFR, and weight affected CL, while weight and FD affected volume. Optimal sampling theory was used to choose pharmacokinetic sampling times for pediatric studies. Doses in patients with renal impairment and in pediatrics were determined by targeting exposure in adults receiving migalastat 123mg every other day. A clinical study was conducted in 20 adolescent patients with FD ≥45kg. eGFR had the largest effect on CL. Simulations showed that exposures in moderate renal impairment were within phase 2-3 exposures; patients aged 2-17 years require weight-based dosing; and predicted exposures in adolescent patients ≥45kg receiving migalastat 123mg every other day were similar to adults (data confirmed in a clinical study). Model-informed drug development optimized dosing and design of clinical studies and supported that no dose adjustments were needed in patients with mild to moderate renal impairment or in adolescent patients ≥45kg.

Review of the Existing Translational Pharmacokinetics Modeling Approaches Specific to Monoclonal Antibodies (mAbs) to Support the First-In-Human (FIH) Dose Selection.

The interest in therapeutic monoclonal antibodies (mAbs) has continuously growing in several diseases. However, their pharmacokinetics (PK) is complex due to their target-mediated drug disposition (TMDD) profiles which can induce a non-linear PK. This point is particularly challenging during the pre-clinical and translational development of a new mAb. This article reviews and describes the existing PK modeling approaches used to translate the mAbs PK from animal to human for intravenous (IV) and subcutaneous (SC) administration routes. Several approaches are presented, from the most empirical models to full physiologically based pharmacokinetic (PBPK) models, with a focus on the population PK methods (compartmental and minimal PBPK models). They include the translational approaches for the linear part of the PK and the TMDD mechanism of mAbs. The objective of this article is to provide an up-to-date overview and future perspectives of the translational PK approaches for mAbs during a model-informed drug development (MIDD), since the field of PK modeling has gained recently significant interest for guiding mAbs drug development.

Phase III dose selection of marzeptacog alfa (activated) informed by population pharmacokinetic modeling: Anovel hemostatic drug.

Marzeptacog alfa (activated) (MarzAA) is an activated recombinant human FVII (rFVIIa) variant developed as subcutaneous (s.c.) administration for the treatment or prevention of bleeding episodes in patients with hemophilia A (HA) or hemophilia B (HB) with inhibitors and other rare bleeding disorders. Population pharmacokinetic (PK) modeling was applied for dose selection for a pivotal phase III clinical trial evaluating s.c. MarzAA for episodic treatment of spontaneous or traumatic bleeding episodes. The population PK model used MarzAA intravenous and s.c. data from previously completed clinical trials in patients with HA/HB with or without inhibitors. Based on the model, clinical trial simulations were performed to predict MarzAA exposure after different dosing regimens. The exposure target was identified using an exposure-matching strategy with a wild-type rFVIIa but adjusting for the difference in potency between the two compounds. Simulations demonstrated a sufficient absorption rate and prolonged exposure following a single 60 μg/kg dose leading to 51% and 70% of the population reaching levels above the target after 3 and 6 h, respectively. According to the phase III protocol, if a second dose was required after 3 h because of a lack of efficacy, 90% of the population was observed to be above target 6 h after the initial dose. The model-informed drug development approach integrated information from several trials and guided dose selection in the pivotal phase III clinical trial for episodic treatment of an acute bleeding event in individuals with HA or HB with inhibitors without the execution of a phase II trial for that indication.

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

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

Transforming Evidence Generation for Drug Label Changes: A Case Study.

Computer Modeling and Simulation (CM&S) provides the opportunity to drastically reduce clinical trial patient burden and advance regulatory decision making. At the suggestion of the US Food and Drug Administration (FDA), MannKind Corporation and Nudge BG submitted an application to the FDA Model-Informed Drug Development (MIDD) pilot program to support a label change for the initial dose of Afrezza® (insulin human), a novel inhalable insulin with a rapid pharmacokinetic and pharmacodynamic profile. The MIDD pilot program demonstrates the FDA's commitment to advancing regulatory science through the adoption of evidence generated by CM&S. A simulation framework was developed based on empirical data. It was used to generate evidence to support the label change. Briefing packages and presentations were prepared for two meetings with the FDA, over a period of four months. The model was thoroughly characterized, determined to be low risk for the question of interest, and submitted along with additional clinical evidence for validation. The FDA found the simulation framework to be helpful in providing insights into the question of interest and provides reasonable glycemic outcome predictions. At the conclusion of the MIDD paired meetings, FDA personnel from the Center for Drug Evaluation and Research review team accepted the simulation and requested additional, traditional clinical evidence to support the proposed label change. In the post-meeting comments, the FDA invited MannKind to submit a proposal for a data package including the CM&S evidence in a Type C meeting for further discussion on the label change. This MIDD pilot experience suggests that CM&S is a credible method for evidence generation. Collaboration between sponsor organizations as well as all stakeholders in the FDA, including proponents of CM&S, can further support regulatory decision-making. The learnings from early participants will allow the program to reach its full potential and thereby ultimately benefit patients, sponsors, and FDA.

Population Pharmacokinetics of Oral Brepocitinib in Healthy Volunteers and Patients.

Brepocitinib is a tyrosine kinase 2 and Janus kinase 1 inhibitor in development for treatment of inflammatory autoimmune diseases. This analysis aimed to add to the pharmacokinetic knowledge of the medication, through development of a population pharmacokinetic model and identification of factors that affect drug disposition. Plasma samples from 5 clinical trials were collated, composed of healthy volunteers, patients with psoriasis and patients with alopecia areata taking oral brepocitinib. NONMEM was used to develop a population pharmacokinetic model, and patient demographics were tested as covariates. The final model was a 1-compartment model with first-order absorption. The typical values for apparent clearance and apparent volume of distribution were 18.7 L/h (78% coefficient of variation [CV]) and 136 L (60.5% CV), respectively. Absorption was rapid with an absorption constant of 3.46 h, with an absorption lag of 0.24 hours observed with the oral tablet formulation. The proportional residual error was found to be 52.7% CV in healthy volunteers and 87.5% CV in patients. High-fat meals were associated with a reduction in both the rate (69.9% lower) and extent (28.3% lower) of absorption, while Asian populations had reduced clearance (24.3% lower). Nonlinear pharmacokinetics were observed at doses of 175 mg and above, with a 35.1% higher relative bioavailability at these doses. There were insufficient data to describe this nonlinearity as a continuous relationship. This initial description of the population pharmacokinetics will act as a foundation for the model-informed drug development of brepocitinib and will facilitate future modeling of this medicine. ClinicalTrials.gov numbers NCT02310750 NCT03236493 NCT03656952 NCT02969018 NCT02974868.

Application of MIDD in Clinical Research of Antitumor Drugs

抗肿瘤药物是新药研发的热点方向，其临床研究具有研发周期长、研发成本和风险高的特点。模型引导的药物开发（model-informed drug development, MIDD）通过建模与模拟，能够对生理学、药理学以及疾病进程等信息进行整合和定量分析，降低药物研发成本，提高临床研究效率。本文以奥希替尼和帕博利珠单抗为例，以问题为导向，阐述MIDD在抗肿瘤药物临床研究各个阶段的具体应用，旨在为MIDD指导抗肿瘤临药物床研究提供一定的借鉴和参考。

Model-Informed Drug Development of New Cefoperazone Sodium and Sulbactam Sodium Combination (3:1): Pharmacokinetic/Pharmacodynamic Analysis and Antibacterial Efficacy Against Enterobacteriaceae.

Objective: Cefoperazone/sulbactam is a commonly used antibiotic combination against the extended-spectrum beta-lactamases (ESBLs)-producing bacteria. The objective of this study was to evaluate the efficacy of a new cefoperazone/sulbactam combination (3:1) for Enterobacteriaceae infection via model-informed drug development (MIDD) approaches. Methods: Sulperazon [cefoperazone/sulbactam (2:1)] was used as a control. Pharmacokinetic (PK) data was collected from a clinical phase I trial. Minimum inhibitory concentrations (MICs) were determined using two-fold broth microdilution method. The percent time that the free drug concentration exceeded the minimum inhibitory concentration (%fT>MIC) was used as the pharmacokinetic/pharmacodynamic indicator correlated with efficacy. Models were developed to characterize the PK profile of cefoperazone and sulbactam. Monte Carlo simulations were employed to determine the investigational regimens of cefoperazone/sulbactam (3:1) for the treatment of infections caused by Enterobacteriaceae based on the probability of target attainment (PTA) against the tested bacteria. Results: Two 2-compartment models were developed to describe the PK profiles of cefoperazone and sulbactam. Simulation results following the single-dose showed that the regimens of cefoperazone/sulbactam combinations in the ratios of 3:1 and 2:1 achieved similar PTA against the tested bacteria. Simulation results from the multiple-dose showed that the dosing regimen of cefoperazone/sulbactam (4 g, TID, 3 g:1 g) showed slightly better antibacterial effect than cefoperazone/sulbactam (6 g, BID, 4 g:2 g) against the Escherichia coli (ESBL−) and Klebsiella pneumoniae (ESBL−). For the other tested bacteria, the above regimens achieved a similar PTA. Conclusions: Cefoperazone/sulbactam (3:1) showed similar bactericidal activity to sulperazon [cefoperazone/sulbactam (2:1)] against the tested bacteria. For the ESBL-producing and cefoperazone-resistant E. coli and K. pneumoniae, Cefoperazone/sulbactam (3:1) did not exhibit advantage as anticipated. Our study indicated that further clinical trials should be carried out cautiously to avoid the potential risks of not achieving the expected target.

Interdisciplinary model-informed drug development for extending duration of elagolix treatment in patients with uterine fibroids.

Elagolix, a gonadotropin-releasing hormone receptor antagonist, was recently approved for heavy menstrual bleeding associated with uterine fibroids (UF, Oriahnn) at a dose of 300 mg twice daily (BID) in combination with add-back therapy (oestradiol 1mg/norethindrone acetate 0.5mg [E2/NETA] once daily) for 24 months use. The limited duration of treatment is related to elagolix dose- and duration-dependent decrease in oestrogen that is mechanistically linked to changes in bone mineral density (BMD). The work herein supported the extended treatment duration of 24 months. An integrated exposure-response and epidemiological modelling framework of elagolix effects on femoral neck BMD (FN-BMD), informed by real-world data and phase 3 clinical trials data, was developed to predict the time course and magnitude of changes in BMD and its relation to risk of bone fracture in women with UF. Model results indicated that women treated with elagolix 300 mg BID + E2/NETA in the long term (ie, >24 months) may experience less than 1% loss in FN-BMD per year, relative to placebo. The exposure-response model simulations and clinical risk factors were used to estimate 10-year risk of fractures using the clinically validated Fracture Risk Assessment Tool (FRAX). The impact of elagolix 300 mg BID + E2/NETA treatment on the 10-year risk of hip or major osteoporotic fractures estimated from the FRAX model was minimal compared to that of placebo. The elagolix integrated exposure-BMD analysis and translation to fracture risk provided an interdisciplinary model-informed drug development framework for clinical benefit-risk evaluation and enabled approval of longer treatment duration to benefit the patient.

Applications of Model Informed Drug Development (MIDD) in Drug Development Lifecycle and Regulatory Review.

Applications of Model Informed Drug Development (MIDD) in Drug Development Lifecycle and Regulatory Review.

Application of in silico methods in clinical research and development of drugs and their formulation, a scoping review (Pub Med Central).

Abstract The research was conducted in March 2022, using the databases Latin American and Caribbean Literature on Health Sciences (Lilacs), National Library of Medicine (PubMed), MEDLINE, Web of Science and Scopus, in English. and Meta-Analyses extension for Scoping Reviews (PRSMA-ScR) Checklist (PRISMA, 2020). The criteria adopted for inclusion and exclusion and the specific keywords/descriptors: drug discovery; in silico clinical trial; computational methods; model-informed drug Discovery and development; virtual patients.