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While some model-informed drug development frameworks are well recognized as enabling clinical trials, the value of disease progression modeling (DPM) in impacting medical product development has yet to be fully realized. The Clinical Trials Transformation Initiative assembled a diverse project team from across the patient, academic, regulatory, and industry sectors of practice to advance the use of DPM for decision making in clinical trials and medical product development. This team conducted a scoping review to explore current applications of DPM and convened a multi-stakeholder expert meeting to discuss its value in medical product development. In this article, we present the scoping review and expert meeting output and propose key questions that medical product developers and regulators may use to inform clinical development strategy, appreciate the therapeutic context and endpoint selection, and optimize trial design with disease progression models. By expanding awareness of the unique value of DPM, this article does not aim to be technical in nature but rather aims to highlight the potential of DPM to improve the quality and efficiency of medical product development., (© 2024 The Author(s). Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Cetuximab was initially developed and approved as a first-line treatment in patients with unresectable metastatic colorectal cancer (mCRC) for weekly administration (250 mg/m 2 Q1W with 400 mg/m 2 loading dose). An every-2-weeks schedule (500 mg/m 2 Q2W) was approved recently by several health authorities. Being synchronized with chemotherapy, Q2W administration should improve patients' convenience and healthcare resource utilization. Herein, we present evidence of non-inferiority of Q2W cetuximab, compared with Q1W dosing using pharmacometrics modeling and clinical trial simulation (CTS). Pooled data from five phase I-III clinical trials in 852 patients with KRAS wild-type mCRC treated with Q1W or Q2W cetuximab were modeled using a population exposure-tumor size (TS) model linked to overall survival (OS); exposure was derived from a previously established population pharmacokinetic model. A semi-mechanistic TS model adapted from the Claret model incorporated killing rate proportional to cetuximab area under the concentration-time curve over 2 weeks (AUC) with Eastern Cooperative Oncology Group (ECOG) status as covariate on baseline TS. The OS was modeled with Weibull hazard using ECOG, baseline TS, primary tumor location, and predicted percent change in TS at 8 weeks as covariates. Model-based simulations revealed indistinguishable early tumor shrinkage and survival between Q2W vs. Q1W cetuximab. CTS evaluated OS non-inferiority (predefined margin of 1.25) in 1,000 trials, each with 2,000 virtual patients receiving Q2W or Q1W cetuximab (1:1), and demonstrated non-inferiority in 94% of cases. Taken together, these analyses provide model-based evidence for clinical non-inferiority of Q2W vs. Q1W cetuximab in mCRC with potential benefits to patients and healthcare providers., (© 2024 Emd Serono Research & Development Institute, Inc. Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

The landscape of oncology drug development has witnessed remarkable advancements over the last few decades, significantly improving clinical outcomes and quality of life for patients with cancer. Project Optimus, introduced by the U.S. Food and Drug Administration, stands as a groundbreaking endeavor to reform dose selection of oncology drugs, presenting both opportunities and challenges for the field. To address complex dose optimization challenges, an Oncology Dose Optimization IQ Working Group was created to characterize current practices, provide recommendations for improvement, develop a clinical toolkit, and engage Health Authorities. Historically, dose selection for cytotoxic chemotherapeutics has focused on the maximum tolerated dose, a paradigm that is less relevant for targeted therapies and new treatment modalities. A survey conducted by this group gathered insights from member companies regarding industry practices in oncology dose optimization. Given oncology drug development is a complex effort with multidimensional optimization and high failure rates due to lack of clinically relevant efficacy, this Working Group advocates for a case-by-case approach to inform the timing, specific quantitative targets, and strategies for dose optimization, depending on factors such as disease characteristics, patient population, mechanism of action, including associated resistance mechanisms, and therapeutic index. This white paper highlights the evolving nature of oncology dose optimization, the impact of Project Optimus, and the need for a tailored and evidence-based approach to optimize oncology drug dosing regimens effectively., (© 2024 The Authors. Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Enpatoran is a novel, highly selective, and potent dual toll-like receptor (TLR)7 and TLR8 inhibitor currently under development for the treatment of autoimmune disorders including systemic lupus erythematosus (SLE), cutaneous lupus erythematosus (CLE), and myositis. The ongoing phase II study (WILLOW; NCT05162586) is evaluating enpatoran for 24 weeks in patients with active SLE or CLE and is currently recruiting. To support development of WILLOW as an Asia-inclusive multiregional clinical trial (MRCT) according to International Conference on Harmonisation E5 and E17 principles, we have evaluated ethnic sensitivity to enpatoran based on clinical pharmacokinetic (PK), pharmacodynamic (PD), and safety data from an ethno-bridging study (NCT04880213), supplemented by relevant quantitative PK, PD, and disease trajectory modeling (DTM) results, and drug metabolism/disease knowledge. A single-center, open-label, sequential dose group study in White and Japanese subjects matched by body weight, height, and sex demonstrated comparable PK and PD properties for enpatoran in Asian vs. non-Asian (White and other) subjects across single 100, 200, and 300 mg orally administered doses. DTM suggested no significant differences in SLE disease trajectory for Asian vs. non-Asian individuals. Aldehyde oxidase (AOX) is considered to be a key contributor to enpatoran metabolism, and a literature review indicated no relevant ethnic differences in AOX function based on in vitro and clinical PK data from marketed drugs metabolized by AOX, supporting the conclusion of low ethnic sensitivity for enpatoran. Taken together, the inclusion of Asian patients in MRCTs including WILLOW was informed based on a Totality of Evidence approach., (© 2024 The Healthcare Business of Merck KGaA. Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

The increasing breadth and depth of resolution in biological and clinical data, including -omics and real-world data, requires advanced analytical techniques like artificial intelligence (AI) and machine learning (ML) to fully appreciate the impact of multi-dimensional population variability in intrinsic and extrinsic factors on disease progression and treatment outcomes. Integration of advanced data analytics in Quantitative Pharmacology is crucial for drug-disease knowledge management, enabling precise, efficient and inclusive drug development and utilization - an application we refer to as model-informed precision medicine. AI/ML enables characterization of the molecular and clinical sources of heterogeneity in disease trajectory, advancing end point qualification and biomarker discovery, and informing patient enrichment for proof-of-concept studies as well as trial designs for efficient evidence generation incorporating digital twins and virtual control arms. Explainable ML methods are valuable in elucidating predictors of efficacy and safety of pharmacological treatments, thereby informing response monitoring and risk mitigation strategies. In oncology, emerging opportunities exist for development of the next generation of disease models via ML-assisted joint longitudinal modeling of high-dimensional biomarker data such as circulating tumor DNA and radiomics profiles as predictors of survival outcomes. Finally, mining real-world data leveraging ML algorithms enables understanding of the impact of exclusion criteria on clinical outcomes, thereby informing rational design of appropriately inclusive clinical trials through data-driven broadening of eligibility criteria. Herein, we provide an overview of the aforementioned contexts of use of ML in drug-disease modeling based on examples across multiple therapeutic areas including neurology, rare diseases, autoimmune diseases, oncology and immuno-oncology., (© 2023 Merck KGaA Darmstadt. Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Xevinapant, an oral inhibitor of apoptosis protein (IAP) inhibitor, demonstrated efficacy in combination with chemoradiotherapy in a randomized phase II study (NCT02022098) in patients with locally advanced squamous cell carcinoma of the head and neck at 200 mg/day on days 1-14 of a 3-week cycle. To confirm 200 mg/day as the recommended phase III dose (RP3D), we integrated preclinical, clinical, pharmacokinetic/pharmacodynamic (PK/PD), and exposure-response modeling results. Population PK/PD modeling of IAP inhibition in peripheral blood mononuclear cells in 21 patients suggested the pharmacologically active dose range was 100-200 mg/day, with a trend for more robust inhibition at the end of the dosing interval at 200 mg/day based on an indirect response model. Additionally, the unbound average plasma concentration at 200 mg/day was similar to that associated with efficacy in preclinical xenograft models. Logistic regression exposure-response analyses of data from 62 patients in the phase II study showed exposure-related increases in probabilities of locoregional control at 18 months (primary end point), overall response, complete response, and the radiosensitization mechanism-related composite safety end point "mucositis and/or dysphagia" (P < 0.05). Exposure-response relationships were not discernible for 12 of 13 evaluated safety end points, incidence of dose reductions, and time to first dose reduction. Quantitative integration of all available data, including model-derived target inhibition profiles, positive exposure-efficacy relationships, and lack of discernible exposure-safety relationships for most safety end points, supports selection of xevinapant 200 mg/day on days 1-14 of a 3-week cycle as the RP3D, allowing for successive dose reductions to 150 and 100 mg/day to manage adverse events., (© 2023 EMD Serono and The Authors. Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Optimizing Ponatinib Treatment in CP-CML (OPTIC) was a randomized, phase II dose-optimization trial of ponatinib in chronic phase-chronic myeloid leukemia (CP-CML) resistant to ≥ 2 tyrosine kinase inhibitors or with T315I mutation. Patients were randomized to starting doses of 45-, 30-, or 15-mg ponatinib once daily. Patients receiving 45- or 30-mg reduced to 15-mg upon achievement of ≤ 1% BCR::ABL1 IS (≥ molecular response with 2-log reduction (MR2)). The exposure-molecular response relationship was described using a four-state, discrete-time Markov model. Time-to-event models were used to characterize the relationship between exposure and arterial occlusive events (AOEs), grade ≥ 3 neutropenia, and thrombocytopenia. Increasing systemic exposures were associated with increasing probability of transitioning from no response to ≥ MR1, and from MR1 to ≥ MR1, with odds ratios of 1.63 (95% confidence interval (CI), 1.06-2.73) and 2.05 (95% CI, 1.53-2.89) for a 15-mg dose increase, respectively. Ponatinib exposure was a significant predictor of AOEs (hazard ratio (HR) 2.05, 95% CI, 1.43-2.93, for a 15-mg dose increase). In the exposure-safety models for neutropenia and thrombocytopenia, exposure was a significant predictor of grade ≥ 3 thrombocytopenia (HR 1.31, 95% CI, 1.05-1.64, for a 15-mg dose increase). Model-based simulations predicted a clinically meaningful higher rate of ≥ MR2 response at 12 months for the 45-mg starting dose (40.4%) vs. 30-mg (34%) and 15-mg (25.2%). The exposure-response analyses supported a ponatinib starting dose of 45 mg with reduction to 15 mg at response for patients with CP-CML., (© 2023 Takeda Pharmaceutical. Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Access lag to innovative therapies in Asian populations continues to present a challenge to global health. Recent progressive changes in the global regulatory landscape, including newer guidelines, are enabling simultaneous global drug development and near-simultaneous global drug registration. The International Conference on Harmonization (ICH) E17 guideline outlines general principles for the design and analysis of multiregional clinical trials (MRCTs). We posit that translational research and quantitative clinical pharmacology tools are core enablers for Asia-inclusive global drug development aligned with ICH E17 principles. Assessment of ethnic sensitivity should be initiated early in the development lifecycle to inform the need for, and extent of, Asian phase I ethno-bridging data. Relevant ethno-bridging data may be generated as standalone Asian phase I trials, as part of Western First-In-Human trials, or under accelerated development settings as a lead-in phase in an MRCT. Quantitative understanding of human clearance mechanisms and pharmacogenetic factors is vital to forecasting ethnic sensitivity in drug exposure using physiologically-based pharmacokinetic models. Stratification factors to control heterogeneity in MRCTs can be identified by reverse translational research incorporating pharmacometric disease models and model-based meta-analyses. Because epidemiological variations can extend to the molecular level, quantitative systems pharmacology models may be useful in forecasting how molecular variation in therapeutic targets or pathway proteins across populations might impact treatment outcomes. Through prospective evaluation of conservation in drug- and disease-related intrinsic and extrinsic factors, a pooled East Asian region can be implemented in Asia-inclusive MRCTs to maximize efficiency in substantiating evidence of benefit-risk for the region at-large with a Totality of Evidence approach., (© 2022 The Authors. Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

The International Consortium for Innovation and Quality (IQ) Physiologically Based Pharmacokinetic (PBPK) Modeling Induction Working Group (IWG) conducted a survey across participating companies around general strategies for PBPK modeling of induction, including experience with its utility to address various questions, regulatory interactions, and regulatory acceptance. The results highlight areas where PBPK modeling is used with high confidence and identifies opportunities where confidence is lower and further evaluation is needed. To enhance the survey results, the PBPK-IWG also collected case studies and analyzed recent literature examples where PBPK models were applied to predict CYP3A induction-mediated drug-drug interactions. PBPK modeling of induction has evolved and progressed significantly, proving to have great potential to accelerate drug discovery and development. With the aim of enabling optimal use for new molecular entities that are either substrates and/or inducers of CYP3A, the PBPK-IWG proposes initial workflows for PBPK application, discusses future trends, and identifies gaps that need to be addressed., (© 2021 The Authors. Clinical Pharmacology & Therapeutics © 2021 American Society for Clinical Pharmacology and Therapeutics.)

The unprecedented challenges posed by the coronavirus disease 2019 (COVID-19) pandemic highlight the urgency for applying clinical pharmacology and model-informed drug development in (i) dosage optimization for COVID-19 therapies, (ii) approaching therapeutic dilemmas in clinical trial settings, and (iii) maximizing value of information from impacted non-COVID-19 trials. More than ever, we have a responsibility for adaptive evidence synthesis with a Totality of Evidence mindset in this race against time across biomedical research, clinical practice, drug development, and regulation., (© 2020 The Authors Clinical Pharmacology & Therapeutics © 2020 American Society for Clinical Pharmacology and Therapeutics.)

Quantitative translational medicine (QTM) is envisioned as a multifaceted discipline that will galvanize the path from idea to medicine through quantitative translation across the discovery, development, regulatory, and utilization spectrum. Here, we summarize results of an American Society for Clinical Pharmacology and Therapeutics (ASCPT) survey on barriers relevant to the advancement of QTM and propose opportunities for its deployment. Importantly, we offer a call to action to break down these barriers through patient-centered stewardship, effective communication, cross-sector collaboration, and a modernized educational curriculum., (© 2019 The Authors Clinical Pharmacology & Therapeutics © 2019 American Society for Clinical Pharmacology and Therapeutics.)

The efficacy of the CD30-directed antibody-drug conjugate (ADC) brentuximab vedotin was established in combination with chemotherapy as frontline treatment for advanced classical Hodgkin's lymphoma in the randomized phase III ECHELON-1 study. Population pharmacokinetic (PK) and exposure-response models were developed to quantify sources of PK variability and relationships between exposure and safety/efficacy end points in ECHELON-1. The influence of patient-specific factors on the PK of the ADC and the microtubule-disrupting payload monomethyl auristatin E (MMAE) was investigated; none of the significant covariates had a clinically relevant impact. Exposure-response analyses evaluated relationships between time-averaged area under the curve (AUC; ADC, MMAE) and efficacy end points (ADC) or safety parameters (ADC, MMAE). Exposure-efficacy analyses supported consistent treatment benefit with brentuximab vedotin across observed exposure ranges. Exposure-safety analyses supported the recommended brentuximab vedotin starting dose (1.2 mg/kg every 2 weeks), and effective management of peripheral neuropathy and neutropenia with dose modification/reduction and febrile neutropenia with granulocyte colony-stimulating factor primary prophylaxis., (© 2019 Millennium Pharmaceuticals. Clinical Pharmacology & Therapeutics published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Model-based meta-analysis (MBMA) is a valuable component of the quantitative pharmacology toolkit for model-informed drug discovery and development. It enables principled decision making with a totality of evidence mindset through integration of internal and external data across multiple dimensions (e.g., targets/mechanisms, molecules/drugs, doses/regimens, diseases/indications, populations, endpoints, and clinical trial designs). MBMA distinguishes itself from traditional meta-analysis by infusing pharmacologic plausibility into the statistical rigor that typifies meta-analytic data integration. This is possible through mechanism-informed formulation of pharmacologically inspired cause-effect and dose-response relationships, time course of treatment effects, and interrelationships between proximal and distal outcomes of modulation of disease biology and pathophysiology. In this review, we offer a question-based approach to enhance appreciation of the value of MBMA across the continuum from drug discovery and translational research through clinical development, comparative effectiveness research, and postapproval optimization of therapeutics using illustrative examples across therapeutic areas., (© 2019 The Authors Clinical Pharmacology & Therapeutics © 2019 American Society for Clinical Pharmacology and Therapeutics.)

The past 2 decades have witnessed transformative changes in our approach to drug-drug interactions (DDIs) from scientific research to clinical practice. Collaborative cooperation across the sectors of academia, pharmaceutical industry, providers of translational research tools and services, global regulatory agencies, and healthcare providers has created and galvanized a science-informed and patient-centered approach. Multidisciplinary innovations in mechanistic assessment of absorption, distribution, metabolism, and excretion (ADME), population pharmacology and pharmacogenetics, physiologically based modeling, and regulatory science have enabled a profound shift in mindset from risk aversion to informative prescribing guidance for optimal risk management. Foundational to this transformation is a commitment to maximizing the value of innovative therapeutics for all patients and across clinical contexts of use through rational knowledge management and translation based on the totality of best available evidence, thereby removing the void that can lead to arbitrary decisions in clinical therapeutics., (© 2019 The Authors Clinical Pharmacology & Therapeutics © 2019 American Society for Clinical Pharmacology and Therapeutics.)

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., (© 2017 The Authors. Clinical Pharmacology & Therapeutics published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.)

The antibody-drug conjugate (ADC) brentuximab vedotin consists of the CD30-directed antibody attached to the microtubule-disrupting agent monomethyl auristatin E (MMAE). In pharmacokinetic models, including data from six studies (380 patients with classical Hodgkin's, systemic anaplastic large-cell, and cutaneous T-cell (CTCL) lymphomas), lower clearance of ADC and modestly higher ADC exposure in CTCL patients did not translate into higher MMAE exposure. In CTCL patients from the phase III ALCANZA study (n = 66), improved progression-free survival with brentuximab vedotin vs. controls was not related to ADC exposure. ADC exposure was a predictor of grade ≥3 treatment-emergent adverse events (TEAEs). Results support the consistent benefit observed with brentuximab vedotin 1.8 mg/kg every 3 weeks across the range of exposures in ALCANZA and support dose reductions in patients experiencing TEAEs at the starting dose., (© 2018 The Authors. Clinical Pharmacology & Therapeutics published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Access to Medicine (AtM) initiatives have traditionally focused on enabling continuous availability and affordability of medicines for patients in the developing world. A more holistic view of AtM across the discovery-development-regulation-utilization continuum would also include participation in the development and timely access to emerging therapies in diverse populations in the era of precision therapeutics, built upon foundational clinical pharmacology principles, leveraging a Totality of Evidence (ToE) mindset., (© 2017 American Society for Clinical Pharmacology and Therapeutics.)

If we are to improve our low success rate and rising costs in the pharmaceutical industry, we need to use every tool available. Reverse translation can particularly inform discovery and early clinical development via appropriate quantitative integration of relevant data. This commentary reports on a crowd-sourced survey (2017) that sought to evaluate the integration of reverse translation in pharma. The results indicate that these methods are being applied, to varying degrees, across most respondents., (© 2017 American Society for Clinical Pharmacology and Therapeutics.)

We live in an era of precision therapeutics, value-based healthcare, patient-participatory research, and enhanced clinical trial transparency, with explosive increases in our ability to access and analyze multiscale biological and clinical data from diverse ecosystems. To discover and develop truly transformative medicines with a patient-centric sense of urgency, we will need to exploit data that lie far beyond the confines of laboratory-based experimental models and controlled clinical trials, dynamically maximizing the value of information in real-world data from clinical practice settings and even social media. This demands commitment to a culture that embraces Reverse Translation as a critical component of the practice of Translational Medicine in the discovery, development, regulation, and utilization of therapeutics., (© 2018 American Society for Clinical Pharmacology and Therapeutics.)

Clinical pharmacodynamic evaluation is a key component of the "pharmacologic audit trail" in oncology drug development. We posit that its value can and should be greatly enhanced via application of a robust quantitative pharmacology framework informed by biologically mechanistic considerations. Herein, we illustrate examples of intersectional blindspots across the disciplines of quantitative pharmacology and translational science and offer a roadmap aimed at enhancing the caliber of clinical pharmacodynamic research in the development of oncology therapeutics., (© 2016 American Society for Clinical Pharmacology and Therapeutics.)

Physiologically based pharmacokinetic (PBPK) modeling and classical population pharmacokinetic (PK) model-based simulations are increasingly used to answer various drug development questions. In this study, we propose a methodology to optimize the development of drugs, primarily cleared by the kidney, using model-based approaches to determine the need for a dedicated renal impairment (RI) study. First, the impact of RI on drug exposure is simulated via PBPK modeling and then confirmed using classical population PK modeling of phase 2/3 data. This methodology was successfully evaluated and applied to an investigational agent, orteronel (nonsteroidal, reversible, selective 17,20-lyase inhibitor). A phase 1 RI study confirmed the accuracy of model-based predictions. Hence, for drugs eliminated primarily via renal clearance, this modeling approach can enable inclusion of patients with RI in phase 3 trials at appropriate doses, which may be an alternative to a dedicated RI study, or suggest that only a reduced-size study in severe RI may be sufficient., (© 2015 The Authors. Clinical Pharmacology & Therapeutics published by Wiley Periodicals, Inc. on behalf of The American Society for Clinical Pharmacology and Therapeutics.)

Despite advances in biomedical research that have deepened our understanding of cancer hallmarks, resulting in the discovery and development of targeted therapies, the success rates of oncology drug development remain low. Opportunities remain for objective dose selection informed by exposure-response understanding to optimize the benefit-risk balance of novel therapies for cancer patients. This review article discusses the principles and applications of modeling and simulation approaches across the lifecycle of development of oncology therapeutics. Illustrative examples are used to convey the value gained from integration of quantitative clinical pharmacology strategies from the preclinical-translational phase through confirmatory clinical evaluation of efficacy and safety., (© 2014 American Society for Clinical Pharmacology and Therapeutics.)

Nine static models (seven basic and two mechanistic) and their respective cutoff values used for predicting cytochrome P450 3A (CYP3A) inhibition, as recommended by the US Food and Drug Administration and the European Medicines Agency, were evaluated using data from 119 clinical studies with orally administered midazolam as a substrate. Positive predictive error (PPE) and negative predictive error (NPE) rates were used to assess model performance, based on a cutoff of 1.25-fold change in midazolam area under the curve (AUC) by inhibitor. For reversible inhibition, basic models using total or unbound systemic inhibitor concentration [I] had high NPE rates (46-47%), whereas those using intestinal luminal ([I]gut) values had no NPE but a higher PPE. All basic models for time-dependent inhibition had no NPE and reasonable PPE rates (15-18%). Mechanistic static models that incorporate all interaction mechanisms and organ specific [I] values (enterocyte and hepatic inlet) provided a higher predictive precision, a slightly increased NPE, and a reasonable PPE. Various cutoffs for predicting the likelihood of CYP3A inhibition were evaluated for mechanistic models, and a cutoff of 1.25-fold change in midazolam AUC appears appropriate.