A model qualification method for mechanistic physiological QSP models to support model-informed drug development.
Ontology highlight
ABSTRACT:
SUBMITTER: Friedrich CM
PROVIDER: S-EPMC4761232 | biostudies-other | 2016 Feb
REPOSITORIES: biostudies-other
Similar Datasets
Project description:Making drug development a more efficient and cost-effective process will have a transformative effect on human health. A key, yet underutilized, tool to aid in this transformation is mechanistic computational modeling. By incorporating decades of hard-won prior knowledge of molecular interactions, cellular signaling, and cellular behavior, mechanistic models can achieve a level of predictiveness that is not feasible using solely empirical characterization of drug pharmacodynamics. These models can integrate diverse types of data from cell culture and animal experiments, including high-throughput systems biology experiments, and translate the results into the context of human disease. This provides a framework for identification of new drug targets, measurable biomarkers for drug action in target tissues, and patient populations for which a drug is likely to be effective or ineffective. Additionally, mechanistic models are valuable in virtual screening of new therapeutic strategies, such as gene or cell therapy and tissue regeneration, identifying the key requirements for these approaches to succeed in a heterogeneous patient population. These capabilities, which are distinct from and complementary to those of existing drug development strategies, demonstrate the opportunity to improve success rates in the drug development pipeline through the use of mechanistic computational models.
| S-EPMC4567444 | biostudies-other
Project description:Pharmacometric analyses are complex and multifactorial. It is essential to check, track, and document the vast amounts of data and metadata that are generated during these analyses (and the relationships between them) in order to comply with regulations, support quality control, auditing, and reporting. It is, however, challenging, tedious, error-prone, and time-consuming, and diverts pharmacometricians from the more useful business of doing science. Automating this process would save time, reduce transcriptional errors, support the retention and transfer of knowledge, encourage good practice, and help ensure that pharmacometric analyses appropriately impact decisions. The ability to document, communicate, and reconstruct a complete pharmacometric analysis using an open standard would have considerable benefits. In this article, the Innovative Medicines Initiative (IMI) Drug Disease Model Resources (DDMoRe) consortium proposes a set of standards to facilitate the capture, storage, and reporting of knowledge (including assumptions and decisions) in the context of model-informed drug discovery and development (MID3), as well as to support reproducibility: "Thoughtflow." A prototype software implementation is provided.
| S-EPMC5445227 | biostudies-literature
Project description:Fenebrutinib is a CYP3A substrate and time-dependent inhibitor, as well as a BCRP and OATP1B transporter inhibitor in vitro. Physiologically-based pharmacokinetic (PBPK) modeling strategies with the ultimate goal of understanding complex drug-drug interactions (DDIs) and proposing doses for untested scenarios were developed. The consistency in the results of two independent approaches, PBPK simulation and endogenous biomarker measurement, supported that the observed transporter DDI is primarily due to fenebrutinib inhibition of intestinal BCRP, rather than hepatic OATP1B. A mechanistic-absorption model accounting for the effects of excipient complexation with fenebrutinib was used to rationalize the unexpected observation of itraconazole-fenebrutinib DDI (maximum plasma concentration (Cmax ) decreased, and area under the curve (AUC) increased). The totality of the evidence from sensitivity analysis and clinical and nonclinical data suggested that fenebrutinib is likely a sensitive CYP3A substrate. This advanced PBPK application allowed the use of model-informed approach to facilitate the development of concomitant medication recommendations for fenebrutinib without requiring additional clinical DDI studies.
| S-EPMC7306618 | biostudies-literature
Project description:Model-informed drug development (MIDD) was central to the development of the oral proteasome inhibitor ixazomib, facilitating internal decisions (switch from body surface area (BSA)-based to fixed dosing, inclusive phase III trials, portfolio prioritization of ixazomib-based combinations, phase III dose for maintenance treatment), regulatory review (model-informed QT analysis, benefit-risk of 4 mg dose), and product labeling (absolute bioavailability and intrinsic/extrinsic factors). This review discusses the impact of MIDD in enabling patient-centric therapeutic optimization during the development of ixazomib.
| S-EPMC6585617 | biostudies-literature
Project description:Background and objectiveMechanistic static and dynamic physiologically based pharmacokinetic models are used in clinical drug development to assess the risk of drug-drug interactions (DDIs). Currently, the use of mechanistic static models is restricted to screening DDI risk for an investigational drug, while dynamic physiologically based pharmacokinetic models are used for quantitative predictions of DDIs to support regulatory filing. As physiologically based pharmacokinetic model development by sponsors as well as a review of models by regulators require considerable resources, we explored the possibility of using mechanistic static models to support regulatory filing, using representative cases of successful physiologically based pharmacokinetic submissions to the US Food and Drug Administration under different classes of applications.MethodsDrug-drug interaction predictions with mechanistic static models were done for representative cases in the different classes of applications using the same data and modelling workflow as described in the Food and Drug Administration clinical pharmacology reviews. We investigated the hypothesis that the use of unbound average steady-state concentrations of modulators as driver concentrations in the mechanistic static models should lead to the same conclusions as those from physiologically based pharmacokinetic modelling for non-dynamic measures of DDI risk assessment such as the area under the plasma concentration-time curve ratio, provided the same input data are employed for the interacting drugs.ResultsDrug-drug interaction predictions of area under the plasma concentration-time curve ratios using mechanistic static models were mostly comparable to those reported in the Food and Drug Administration reviews using physiologically based pharmacokinetic models for all representative cases in the different classes of applications.ConclusionsThe results reported in this study should encourage the use of models that best fit an intended purpose, limiting the use of physiologically based pharmacokinetic models to those applications that leverage its unique strengths, such as what-if scenario testing to understand the effect of dose staggering, evaluating the role of uptake and efflux transporters, extrapolating DDI effects from studied to unstudied populations, or assessing the impact of DDIs on the exposure of a victim drug with concurrent mechanisms. With this first step, we hope to trigger a scientific discussion on the value of a routine comparison of the two methods for regulatory submissions to potentially create a best practice that could help identify examples where the use of dynamic changes in modulator concentrations could make a difference to DDI risk assessment.
| S-EPMC10042977 | biostudies-literature
Project description:Sickle cell disease (SCD) is characterized by the production of sickle hemoglobin (HbS), which when deoxygenated, polymerizes leading to red blood cell damage and hemolytic anemia, a defining feature of SCD. Voxelotor (Oxbryta) is a small molecule inhibitor of HbS polymerization that disrupts the polymerization mechanism by binding HbS to increase HbS oxygen affinity. Voxelotor is approved in the United States for the treatment of SCD in patients greater than or equal to 12 years of age at a 1500 mg once-daily (q.d.) dose. These exposure-response analyses aimed to evaluate the relationships between voxelotor whole blood concentration and change from baseline (CFB) in clinical measures of anemia and hemolysis and between voxelotor whole blood and plasma concentrations and the incidence of selected safety end points to confirm the voxelotor mechanism of action and to support the clinical dose recommendation. In patients treated with voxelotor up to 72 weeks, CFB hemoglobin (Hb) increased linearly (p < 0.001) with increasing voxelotor concentration and percent Hb occupancy and increases in CFB Hb corresponded to improvements in measures of hemolysis. The target 1 g/dl increase in CFB Hb was achieved with 1500 mg voxelotor q.d. Significant relationships were observed between voxelotor exposures and grade greater than or equal to 1 increased alanine aminotransferase and decreased white blood cell count; however, most events were grade 1. No clinically important covariate effects on voxelotor efficacy or safety were observed. Overall, these analyses support 1500 mg q.d. as the therapeutic dose for voxelotor in adults and adolescents.
| S-EPMC9197532 | biostudies-literature
Project description:Everolimus is currently approved in Europe as an adjunctive therapy for patients aged ≥ 2 years with tuberous sclerosis complex (TSC)-associated treatment-refractory partial-onset seizures, based on the EXIST-3 study (NCT01713946) results. As TSC-associated seizures can also affect children aged between 6 months and 2 years, a modeling and simulation (M&S) approach was undertaken to extrapolate exposure (trough plasma concentration (Cmin )) after a dose of 6 mg/m2 and reduction in seizure frequency (RSF). A physiologically based pharmacokinetic model using Simcyp was developed to predict Cmin in adult and pediatric patients, which was then used by a population pharmacodynamic model and a linear mixed effect model to predict short-term and long-term efficacy in adults (for validation) and in children, respectively. Based on the results of the M&S study, everolimus at the dose of 6 mg/m2 is anticipated to be an efficacious treatment in children 6 months to 2 years of age (up to 77.8% RSF) with concentrations within the recommended target range.
| S-EPMC7180003 | biostudies-literature
Project description: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 240 mg Q3W flat dose relative to the currently approved 3 mg/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 240 mg Q3W dosing regimen were comparable to those for the 3 mg/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 240 mg Q3W dosing regimen was comparable to 3 mg/kg Q2W dosing regimen. The safety and efficacy E-R results of 240 mg Q3W is flat. Hence, the 240 mg Q3W dosing regimen is determined to be a preferred therapeutic dosage for toripalimab due to the convenience of flat dose.
| S-EPMC9880172 | biostudies-literature
Project description:ObjectiveTo summarize applications of natural language processing (NLP) in model informed drug development (MIDD) and identify potential areas of improvement.Materials and methodsPublications found on PubMed and Google Scholar, websites and GitHub repositories for NLP libraries and models. Publications describing applications of NLP in MIDD were reviewed. The applications were stratified into 3 stages: drug discovery, clinical trials, and pharmacovigilance. Key NLP functionalities used for these applications were assessed. Programming libraries and open-source resources for the implementation of NLP functionalities in MIDD were identified.ResultsNLP has been utilized to aid various processes in drug development lifecycle such as gene-disease mapping, biomarker discovery, patient-trial matching, adverse drug events detection, etc. These applications commonly use NLP functionalities of named entity recognition, word embeddings, entity resolution, assertion status detection, relation extraction, and topic modeling. The current state-of-the-art for implementing these functionalities in MIDD applications are transformer models that utilize transfer learning for enhanced performance. Various libraries in python, R, and Java like huggingface, sparkNLP, and KoRpus as well as open-source platforms such as DisGeNet, DeepEnroll, and Transmol have enabled convenient implementation of NLP models to MIDD applications.DiscussionChallenges such as reproducibility, explainability, fairness, limited data, limited language-support, and security need to be overcome to ensure wider adoption of NLP in MIDD landscape. There are opportunities to improve the performance of existing models and expand the use of NLP in newer areas of MIDD.ConclusionsThis review provides an overview of the potential and pitfalls of current NLP approaches in MIDD.
| S-EPMC9188322 | biostudies-literature
Project description:Model-informed drug development (MIDD) has a long and rich history in infectious diseases. This review describes foundational principles of translational anti-infective pharmacology, including choice of appropriate measures of exposure and pharmacodynamic (PD) measures, patient subpopulations, and drug-drug interactions. Examples are presented for state-of-the-art, empiric, mechanistic, interdisciplinary, and real-world evidence MIDD applications in the development of antibacterials (review of minimum inhibitory concentration-based models, mechanism-based pharmacokinetic/PD (PK/PD) models, PK/PD models of resistance, and immune response), antifungals, antivirals, drugs for the treatment of global health infectious diseases, and medical countermeasures. The degree of adoption of MIDD practices across the infectious diseases field is also summarized. The future application of MIDD in infectious diseases will progress along two planes; "depth" and "breadth" of MIDD methods. "MIDD depth" refers to deeper incorporation of the specific pathogen biology and intrinsic and acquired-resistance mechanisms; host factors, such as immunologic response and infection site, to enable deeper interrogation of pharmacological impact on pathogen clearance; clinical outcome and emergence of resistance from a pathogen; and patient and population perspective. In particular, improved early assessment of the emergence of resistance potential will become a greater focus in MIDD, as this is poorly mitigated by current development approaches. "MIDD breadth" refers to greater adoption of model-centered approaches to anti-infective development. Specifically, this means how various MIDD approaches and translational tools can be integrated or connected in a systematic way that supports decision making by key stakeholders (sponsors, regulators, and payers) across the entire development pathway.
| S-EPMC8014105 | biostudies-literature