Model-Informed Approaches to Support Drug Development for Patients With Obesity: A Regulatory Perspective.

Obesity, which is defined as having a body mass index of 30 kg/m2 or greater, has been recognized as a serious health problem that increases the risk of many comorbidities (eg, heart disease, stroke, and diabetes) and mortality. The high prevalence of individuals who are classified as obese calls for additional considerations in clinical trial design. Nevertheless, gaining a comprehensive understanding of how obesity affects the pharmacokinetics (PK), pharmacodynamics (PD), and efficacy of drugs proves challenging, primarily as obese patients are seldom selected for enrollment at the early stages of drug development. Over the past decade, model-informed drug development (MIDD) approaches have been increasingly used in drug development programs for obesity and its related diseases as they use and integrate all available sources and knowledge to inform and facilitate clinical drug development. This review summarizes the impact of obesity on PK, PD, and the efficacy of drugs and, more importantly, provides an overview of the use of MIDD approaches in drug development and regulatory decision making for patients with obesity: estimating PK, PD, and efficacy in specific dosing scenarios, optimizing dose regimen, and providing evidence for seeking new indication(s). Recent review cases using MIDD approaches to support dose selection and provide confirmatory evidence for effectiveness for patients with obesity, including pediatric patients, are discussed. These examples demonstrate the promise of MIDD as a valuable tool in supporting clinical trial design during drug development and facilitating regulatory decision-making processes for the benefit of patients with obesity.

Model-Informed Drug Development Approaches to Assist New Drug Development in the COVID-19 Pandemic.

Leveraging limited clinical and nonclinical data through modeling approaches facilitates new drug development and regulatory decision making amid the coronavirus disease 2019 (COVID-19) pandemic. Model-informed drug development (MIDD) is an essential tool to integrate those data and generate evidence to (i) provide support for effectiveness in repurposed or new compounds to combat COVID-19 and dose selection when clinical data are lacking; (ii) assess efficacy under practical situations such as dose reduction to overcome supply issues or emergence of resistant variant strains; (iii) demonstrate applicability of MIDD for full extrapolation to adolescents and sometimes to young pediatric patients; and (iv) evaluate the appropriateness for prolonging a dosing interval to reduce the frequency of hospital visits during the pandemic. Ongoing research activities of MIDD reflect our continuous effort and commitment in bridging knowledge gaps that leads to the availability of effective treatments through innovation. Case examples are presented to illustrate how MIDD has been used in various stages of drug development and has the potential to inform regulatory decision making.

Whole Body PBPK Modeling of Remdesivir and Its Metabolites to Aid in Estimating Active Metabolite Exposure in the Lung and Liver in Patients With Organ Dysfunction.

Remdesivir (RDV) is the first drug approved by the US Food and Drug Administration (FDA) for the treatment of coronavirus disease 2019 (COVID-19) in certain patients requiring hospitalization. As a nucleoside analogue prodrug, RDV undergoes intracellular multistep activation to form its pharmacologically active species, GS-443902, which is not detectable in the plasma. A question arises that whether the observed plasma exposure of RDV and its metabolites would correlate with or be informative about the exposure of GS-443902 in tissues. A whole body physiologically-based pharmacokinetic (PBPK) modeling and simulation approach was utilized to elucidate the disposition mechanism of RDV and its metabolites in the lungs and liver and explore the relationship between plasma and tissue pharmacokinetics (PK) of RDV and its metabolites in healthy subjects. In addition, the potential alteration of plasma and tissue PK of RDV and its metabolites in patients with organ dysfunction was explored. Our simulation results indicated that intracellular exposure of GS-443902 was decreased in the liver and increased in the lungs in subjects with hepatic impairment relative to the subjects with normal liver function. In subjects with severe renal impairment, the exposure of GS-443902 in the liver was slightly increased, whereas the lung exposure of GS-443902 was not impacted. These predictions along with the organ impairment study results may be used to support decision making regarding the RDV dosage adjustment in these patient subgroups. The modeling exercise illustrated the potential of whole body PBPK modeling to aid in decision making for nucleotide analogue prodrugs, particularly when the active metabolite exposure in the target tissues is not available.

Quantitative systems pharmacology: Landscape analysis of regulatory submissions to the US Food and Drug Administration.

Quantitative systems pharmacology (QSP) has been proposed as a scientific domain that can enable efficient and informative drug development. During the past several years, there has been a notable increase in the number of regulatory submissions that contain QSP, including Investigational New Drug Applications (INDs), New Drug Applications (NDAs), and Biologics License Applications (BLAs) to the US Food and Drug Administration. However, there has been no comprehensive characterization of the nature of these regulatory submissions regarding model details and intended applications. To address this gap, a landscape analysis of all the QSP submissions as of December 2020 was conducted. This report summarizes the (1) yearly trend of submissions, (2) proportion of submissions between INDs and NDAs/BLAs, (3) percentage distribution along the stages of drug development, (4) percentage distribution across various therapeutic areas, and (5) nature of QSP applications. In brief, QSP is increasingly applied to model and simulate both drug effectiveness and safety throughout the drug development process across disease areas.

FDA-Industry Scientific Exchange on assessing quantitative systems pharmacology models in clinical drug development: a meeting report, summary of challenges/gaps, and future perspective.

The pharmaceutical industry is actively applying quantitative systems pharmacology (QSP) to make internal decisions and guide drug development. To facilitate the eventual development of a common framework for assessing the credibility of QSP models for clinical drug development, scientists from US Food and Drug Administration and the pharmaceutical industry organized a full-day virtual Scientific Exchange on July 1, 2020. An assessment form was used to ensure consistency in the evaluation process. Among the cases presented, QSP was applied to various therapeutic areas. Applications mostly focused on phase 2 dose selection. Model transparency, including details on expert knowledge and data used for model development, was identified as a major factor for robust model assessment. The case studies demonstrated some commonalities in the workflow of QSP model development, calibration, and validation but differ in the size, scope, and complexity of QSP models, in the acceptance criteria for model calibration and validation, and in the algorithms/approaches used for creating virtual patient populations. Though efforts are being made to build the credibility of QSP models and the confidence is increasing in applying QSP for internal decisions at the clinical stages of drug development, there are still many challenges facing QSP application to late stage drug development. The QSP community needs a strategic plan that includes the ability and flexibility to Adapt, to establish Common expectations for model Credibility needed to inform drug Labeling and patient care, and to AIM to achieve the goal (ACCLAIM).

Effect of Body Weight and Age on the Pharmacokinetics of Dihydroartemisinin: Food and Drug Administration Basis for Dose Determination of Artesunate for Injection in Pediatric Patients With Severe Malaria.

For treatment of severe malaria, the World Health Organization recommends 3 mg/kg intravenous artesunate in pediatric patients weighing less than 20 kg. Here we describe the Food and Drug Administration's rationale for selecting 2.4 mg/kg in pediatric patients weighing less than 20 kg based on literature review and independent analyses.

Translational Quantitative Systems Pharmacology in Drug Development: from Current Landscape to Good Practices.

Systems pharmacology approaches have the capability of quantitatively linking the key biological molecules relevant to a drug candidate's mechanism of action (drug-induced signaling pathways) to the clinical biomarkers associated with the proposed target disease, thereby quantitatively facilitating its development and life cycle management. In this review, the model attributes of published quantitative systems pharmacology (QSP) modeling for lowering cholesterol, treating salt-sensitive hypertension, and treating rare diseases as well as describing bone homeostasis and related pharmacological effects are critically reviewed with respect to model quality, calibration, validation, and performance. We further reviewed the common practices in optimizing QSP modeling and prediction. Notably, leveraging genetics and genomic studies for model calibration and validation is common. Statistical and quantitative assessment of QSP prediction and handling of model uncertainty are, however, mostly lacking as are the quantitative and statistical criteria for assessing QSP predictions and the covariance matrix of coefficients between the parameters in a validated virtual population. To accelerate advances and application of QSP with consistent quality, a list of key questions is proposed to be addressed when assessing the quality of a QSP model in hopes of stimulating the scientific community to set common expectations. The common expectations as to what constitutes the best QSP modeling practices, which the scientific community supports, will advance QSP modeling in the realm of informed drug development. In the long run, good practices will extend the life cycles of QSP models beyond the life cycles of individual drugs.

The Utility of a Population Approach in Drug-Drug Interaction Assessments: A Simulation Evaluation.

This study aims at evaluating the utility of the population pharmacokinetics approach in therapeutic protein drug-drug-interaction (DDI) assessment. Simulations were conducted for 2 representative victim drugs, methotrexate and trastuzumab, using a parallel-group design with and without the interaction drug. The effect of a perpetrator on the exposure of the victim drug is described as the ratio of clearance/apparent clearance of the victim drug given with or without the perpetrator. The power of DDI assessment was calculated as the percentage of runs with 90% confidence interval of the estimated DDI effect within 80% to 125% for the scenarios of no DDI, benchmarked with the noncompartmental approach with intensive sampling. The impact of the number of subjects, the number of sampling points per subject, sampling time error, and model misspecification on the power of DDI determination were evaluated. Results showed that with equal numbers of subjects in each arm, the population pharmacokinetics approach with sparse sampling may need about the same or a higher number of subjects compared to a noncompartmental approach in order to achieve similar power. Increasing the number of subjects, even if only in the study drug alone arm, can increase the power. Sampling or dosing time error had notable impacts on the power for methotrexate but not for trastuzumab. Model misspecification had no notable impacts on the power for trastuzumab. Overall, the population pharmacokinetics approach with sparse sampling built in phase 2/3 studies allows appropriate DDI assessment with adequate study design and analysis and can be considered as an alternative to dedicated DDI studies.

Esomeprazole FDA Approval in Children With GERD: Exposure-Matching and Exposure-Response.

OBJECTIVES: Food and Drug Administration approval of proton-pump inhibitors for infantile gastroesophageal reflux disease has been limited by intrapatient variability in the clinical assessment of gastroesophageal reflux disease. For children 1 to 17 years old, extrapolating efficacy from adults for IV esomeprazole was accepted. The oral formulation was previously approved in children. Exposure-response and exposure matching analyses were sought to identify approvable pediatric doses. METHODS: Intragastric pH biomarker comparisons between children and adults were conducted. Pediatric doses were selected to match exposures in adults and were based on population pharmacokinetic (PK) modeling and simulations with pediatric esomeprazole data. Observed IV or oral esomeprazole PK data were available from 50 and 117 children, between birth and 17 years, respectively, and from 65 adults, between 20 and 48 years. A population PK model developed using these data was used to simulate steady-state esomeprazole exposures for children at different doses to match the observed exposures in adults. RESULTS: Exposure-response relationships of intragastric pH measures were similar between children and adults. The PK simulations identified a dosing regimen for children that results in comparable steady-state area under the curve to that observed after 20 mg in adults. For IV esomeprazole, increasing the infusion duration to 10 to 30 minutes in children achieves matching Cmax values with adults. CONCLUSIONS: The exposure-matching analysis permitted approval of an esomeprazole regimen not studied directly in clinical trials. Exposure-response for intragastric pH-permitted approval for the treatment of gastroesophageal reflux disease in children in whom it was not possible to evaluate the adult primary endpoint, mucosal healing assessed by endoscopy.

Role of Quantitative Clinical Pharmacology in Pediatric Approval and Labeling.

Dose selection is one of the key decisions made during drug development in pediatrics. There are regulatory initiatives that promote the use of model-based drug development in pediatrics. Pharmacometrics or quantitative clinical pharmacology enables development of models that can describe factors affecting pharmacokinetics and/or pharmacodynamics in pediatric patients. This manuscript describes some examples in which pharmacometric analysis was used to support approval and labeling in pediatrics. In particular, the role of pharmacokinetic (PK) comparison of pediatric PK to adults and utilization of dose/exposure-response analysis for dose selection are highlighted. Dose selection for esomeprazole in pediatrics was based on PK matching to adults, whereas for adalimumab, exposure-response, PK, efficacy, and safety data together were useful to recommend doses for pediatric Crohn's disease. For vigabatrin, demonstration of similar dose-response between pediatrics and adults allowed for selection of a pediatric dose. Based on model-based pharmacokinetic simulations and safety data from darunavir pediatric clinical studies with a twice-daily regimen, different once-daily dosing regimens for treatment-naïve human immunodeficiency virus 1-infected pediatric subjects 3 to <12 years of age were evaluated. The role of physiologically based pharmacokinetic modeling (PBPK) in predicting pediatric PK is rapidly evolving. However, regulatory review experiences and an understanding of the state of science indicate that there is a lack of established predictive performance of PBPK in pediatric PK prediction. Moving forward, pharmacometrics will continue to play a key role in pediatric drug development contributing toward decisions pertaining to dose selection, trial designs, and assessing disease similarity to adults to support extrapolation of efficacy.

Methods and strategies for assessing uncontrolled drug-drug interactions in population pharmacokinetic analyses: results from the International Society of Pharmacometrics (ISOP) Working Group.

The purpose of this work was to present a consolidated set of guidelines for the analysis of uncontrolled concomitant medications (ConMed) as a covariate and potential perpetrator in population pharmacokinetic (PopPK) analyses. This white paper is the result of an industry-academia-regulatory collaboration. It is the recommendation of the working group that greater focus be given to the analysis of uncontrolled ConMeds as part of a PopPK analysis of Phase 2/3 data to ensure that the resulting outcome in the PopPK analysis can be viewed as reliable. Other recommendations include: (1) collection of start and stop date and clock time, as well as dose and frequency, in Case Report Forms regarding ConMed administration schedule; (2) prespecification of goals and the methods of analysis, (3) consideration of alternate models, other than the binary covariate model, that might more fully characterize the interaction between perpetrator and victim drug, (4) analysts should consider whether the sample size, not the percent of subjects taking a ConMed, is sufficient to detect a ConMed effect if one is present and to consider the correlation with other covariates when the analysis is conducted, (5) grouping of ConMeds should be based on mechanism (e.g., PGP-inhibitor) and not drug class (e.g., beta-blocker), and (6) when reporting the results in a publication, all details related to the ConMed analysis should be presented allowing the reader to understand the methods and be able to appropriately interpret the results.

Modeling effects of dexamethasone on disease progression of bone mineral density in collagen-induced arthritic rats.

A mechanism-based model was developed to characterize the crosstalk between proinflammatory cytokines, bone remodeling biomarkers, and bone mineral density (BMD) in collagen-induced arthritic (CIA) rats. Male Lewis rats were divided into five groups: healthy control, CIA control, CIA receiving single 0.225 mg kg(-1) subcutaneous (SC) dexamethasone (DEX), CIA receiving single 2.25 mg kg(-1) SC DEX, and CIA receiving chronic 0.225 mg kg(-1) SC DEX. The CIA rats underwent collagen induction at day 0 and DEX was injected at day 21 post-induction. Disease activity was monitored throughout the study and rats were sacrificed at different time points for blood and paw collection. Protein concentrations of interleukin (IL)-1ß, IL-6, receptor activator of nuclear factor kappa-B ligand (RANKL), osteoprotegerin (OPG), and tartrate-resistant acid phosphatase 5b (TRACP-5b) in paws were measured by enzyme-linked immunosorbent assays (ELISA). Disease progression and DEX pharmacodynamic profiles of IL-1ß, IL-6, RANKL, and OPG were fitted simultaneously and parameters were sequentially applied to fit the TRACP-5b and BMD data. The model was built according to the mechanisms reported in the literature and modeling was performed using ADAPT 5 software with naïve pooling. Time profiles of IL-1ß and IL-6 protein concentrations correlated with their mRNAs. The RANKL and OPG profiles matched previous findings in CIA rats. DEX inhibited the expressions of IL-1ß, IL-6, and RANKL, but did not alter OPG. TRACP-5b was also inhibited by DEX. Model predictions suggested that anti-IL-1ß therapy and anti-RANKL therapy would result in similar efficacy for prevention of bone loss among the cytokine antagonists.

Utility of population pharmacokinetic modeling in the assessment of therapeutic protein-drug interactions.

Assessment of pharmacokinetic (PK) based drug-drug interactions (DDI) is essential for ensuring patient safety and drug efficacy. With the substantial increase in therapeutic proteins (TP) entering the market and drug development, evaluation of TP-drug interaction (TPDI) has become increasingly important. Unlike for small molecule (e.g., chemical-based) drugs, conducting TPDI studies often presents logistical challenges, while the population PK (PPK) modeling may be a viable approach dealing with the issues. A working group was formed with members from the pharmaceutical industry and the FDA to assess the utility of PPK-based TPDI assessment including study designs, data analysis methods, and implementation strategy. This paper summarizes key issues for consideration as well as a proposed strategy with focuses on (1) PPK approach for exploratory assessment; (2) PPK approach for confirmatory assessment; (3) importance of data quality; (4) implementation strategy; and (5) potential regulatory implications. Advantages and limitations of the approach are also discussed.

Therapeutic protein drug-drug interactions: navigating the knowledge gaps-highlights from the 2012 AAPS NBC Roundtable and IQ Consortium/FDA workshop.

The investigation of therapeutic protein drug-drug interactions has proven to be challenging. In May 2012, a roundtable was held at the American Association of Pharmaceutical Scientists National Biotechnology Conference to discuss the challenges of preclinical assessment and in vitro to in vivo extrapolation of these interactions. Several weeks later, a 2-day workshop co-sponsored by the U.S. Food and Drug Administration and the International Consortium for Innovation and Quality in Pharmaceutical Development was held to facilitate better understanding of the current science, investigative approaches and knowledge gaps in this field. Both meetings focused primarily on drug interactions involving therapeutic proteins that are pro-inflammatory cytokines or cytokine modulators. In this meeting synopsis, we provide highlights from both meetings and summarize observations and recommendations that were developed to reflect the current state of the art thinking, including a four-step risk assessment that could be used to determine the need (or not) for a dedicated clinical pharmacokinetic interaction study.

Impact of pharmacometric analyses on new drug approval and labelling decisions: a review of 198 submissions between 2000 and 2008.

Pharmacometric analyses have become an increasingly important component of New Drug Application (NDA) and Biological License Application (BLA) submissions to the US FDA to support drug approval, labelling and trial design decisions. Pharmacometrics is defined as a science that quantifies drug, disease and trial information to aid drug development, therapeutic decisions and/or regulatory decisions. In this report, we present the results of a survey evaluating the impact of pharmacometric analyses on regulatory decisions for 198 submissions during the period from 2000 to 2008. Pharmacometric review of NDAs included independent, quantitative analyses by FDA pharmacometricians, even when such analysis was not conducted by the sponsor, as well as evaluation of the sponsor's report. During 2000-2008, the number of reviews with pharmacometric analyses increased dramatically and the number of reviews with an impact on approval and labelling also increased in a similar fashion. We also present the impact of pharmacometric analyses on selection of paediatric dosing regimens, approval of regimens that had not been directly studied in clinical trials and provision of evidence of effectiveness to support a single pivotal trial. Case studies are presented to better illustrate the role of pharmacometric analyses in regulatory decision making.

Down-regulation of heat shock protein 70 improves arsenic trioxide and 17-DMAG effects on constitutive signal transducer and activator of transcription 3 activity.

PURPOSE: Signal transducer and activator of transcription 3 (STAT3) has been shown to be constitutively active in approximately 50% of patients with acute myeloid leukemia and is associated with worse outcome. Arsenic trioxide (ATO) synergizes with the heat shock protein (HSP) 90 inhibitor, 17-DMAG, to down-regulate STAT3 activity. However, both agents up-regulate HSP70, an anti-apoptotic protein. We therefore examined whether down-regulating HSP70 with short interference (si) RNA will affect ATO and 17-DMAG effects on constitutive STAT3 activity. EXPERIMENTAL DESIGN: A semi-mechanistic pharmacodynamic model was used to characterize concentration-effect relationships of ATO and 17-DMAG effects on constitutive STAT3 activity and HSP70 expression with or without siRNA against HSP70 in a cell line model. RESULTS: Treatment with siRNA for HSP70 resulted in a stronger degree of synergism on down-regulation of STAT3 activity by ATO and 17-DMAG. However, treatment with siRNA for HSP70 resulted in less synergism on up-regulation of HSP70 by the two drugs. CONCLUSIONS: Down-regulation of HSP70 improves ATO and 17-DMAG effects on constitutive STAT3 activity. These results further provide a basis for studying the combined role of ATO with a HSP90 inhibitor such as 17-DMAG in AML with constitutive STAT3 activity.

Quantitative dynamic models of arthritis progression in the rat.

PURPOSE: This comparison employs mathematical disease progression models to identify a rat model of arthritis with the least inter-animal variability and features lending to better study designs. METHODS: Arthritis was induced with either collagen (CIA) or mycobacterium (AIA) in either Lewis or Dark Agouti (DA) rats. Disease progression was monitored by paw edema and body weight. Models with production, loss, and feedback components were constructed and population analysis using NONMEM software was employed to identify inter-animal variability in the various disease progression parameters. RESULTS: Onset time was the only parameter different within all four groups (DA-AIA 11.5 days, DA-CIA 16.5 days, Lewis-AIA 11.9 days, Lewis-CIA 13.9 days). The loss-of-edema rate constant was 20% slower in DA (0.362 h(-1)) than Lewis (0.466 h(-1)) rats. Most models exhibited peak paw edema 20 days post-induction. Edema in CIA returned to 150% of the initial value after the disease peaked. DA rats displayed more severe overall responses. CONCLUSIONS: No statistical differences between groups were observed for inter-animal variation in disease onset, progression and severity parameters. Onset time varies and should be noted in the design of future studies. DA rats may offer a more dynamic range of edema response than Lewis rats.