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

Chow, Andrew; Earp, Justin; Gupta, Manish; Hanley, William D.; Hu, Chuanpu; Wang, Diane D.; Zajic, Stefan; Zhu, Min

doi:10.1002/jcph.240

Cited by 16 publications

(14 citation statements)

References 36 publications

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“…The reason behind the lack of effect of concomitant medications on vedolizumab CL L is not currently understood. A sensitivity analysis was performed to determine whether the rate of concomitant medication use in the vedolizumab population pharmacokinetic data set was sufficient to achieve at least 80% power to detect no drug interaction, as recommended by the Population Pharmacokinetic Therapeutic Protein–Drug Interaction (PK TPDI) Working Group . This analysis revealed that the data set met the sample size requirements to ensure at least 80% power and confirmed that vedolizumab CL L was not impacted by concomitant use of azathioprine, mercaptopurine, methotrexate or aminosalicylates…”

Section: Discussionmentioning

confidence: 94%

Population pharmacokinetics-pharmacodynamics of vedolizumab in patients with ulcerative colitis and Crohn's disease

Rosario

Dirks

Gastonguay

et al. 2015

Aliment Pharmacol Ther

224

260

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SummaryBackgroundVedolizumab, an anti‐α4β7 integrin monoclonal antibody (mAb), is indicated for treating patients with moderately to severely active ulcerative colitis (UC) and Crohn's disease (CD). As higher therapeutic mAb concentrations have been associated with greater efficacy in inflammatory bowel disease, understanding determinants of vedolizumab clearance may help to optimise dosing.AimsTo characterise vedolizumab pharmacokinetics in patients with UC and CD, to identify clinically relevant determinants of vedolizumab clearance, and to describe the pharmacokinetic–pharmacodynamic relationship using population modelling.MethodsData from a phase 1 healthy volunteer study, a phase 2 UC study, and 3 phase 3 UC/CD studies were included. Population pharmacokinetic analysis for repeated measures was conducted using nonlinear mixed effects modelling. Results from the base model, developed using extensive phase 1 and 2 data, were used to develop the full covariate model, which was fit to sparse phase 3 data.ResultsVedolizumab pharmacokinetics was described by a 2‐compartment model with parallel linear and nonlinear elimination. Using reference covariate values, linear elimination half‐life of vedolizumab was 25.5 days; linear clearance (CLL) was 0.159 L/day for UC and 0.155 L/day for CD; central compartment volume of distribution (V c) was 3.19 L; and peripheral compartment volume of distribution was 1.66 L. Interindividual variabilities (%CV) were 35% for CLL and 19% for V c; residual variance was 24%. Only extreme albumin and body weight values were identified as potential clinically important predictors of CLL.ConclusionsPopulation pharmacokinetic parameters were similar in patients with moderately to severely active UC and CD. This analysis supports use of vedolizumab fixed dosing in these patients. Clinicaltrials.gov Identifiers: NCT01177228; NCT00783718 (GEMINI 1); NCT00783692 (GEMINI 2); NCT01224171 (GEMINI 3).

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Section: Discussionmentioning

confidence: 94%

Population pharmacokinetics-pharmacodynamics of vedolizumab in patients with ulcerative colitis and Crohn's disease

Rosario

Dirks

Gastonguay

et al. 2015

Aliment Pharmacol Ther

224

260

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“…Options for clinical assessment of DDIs include a population PK approach using data from multiple phase 3 studies, which should suffice to recommend dosing adjustments when the signal of interaction is clear and large. 83 No formal DDI studies have been conducted for nivolumab. However, its DDI potential was indirectly assessed by measuring the expression of a number of cytokines, including those known to affect CYP450 enzyme activity [79][80][81] over the dose range of 0.3 to 10 mg/kg every 3 weeks.…”

Section: Drug-drug Interaction Potentialmentioning

confidence: 99%

Clinical Pharmacology Considerations for the Development of Immune Checkpoint Inhibitors

Sheng

Srivastava

Sanghavi

et al. 2017

The Journal of Clinical Pharma

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Immuno-oncology works through activation of the patient's immune system against cancer, with several advantages over other treatment approaches, including cytotoxic agents and molecular-targeted therapies. The most notable feature of immuno-oncology treatments is the nature of the patient responses achieved, which can be more durable and sustained than with other modalities. Increased understanding of immune system complexity has provided a number of opportunities to advance several strategies for the development of immuno-oncology therapies. This review outlines the clinical pharmacology characteristics and development challenges for the 6 approved immunomodulatory monoclonal antibodies that target 2 immune checkpoint pathways: ipilimumab (an anti-cytotoxic T-lymphocyte antigen-4 antibody) and, more recently, nivolumab and pembrolizumab (both anti-programmed death-1 antibodies) and atezolizumab, avelumab, and durvalumab (all anti-programmed death ligand-1 antibodies). These agents have revealed much about the clinical pharmacology features of immune checkpoint inhibitors as a class, as well as the pharmacometric approaches used to support their clinical development and regulatory approval. The development experiences with these pioneering immuno-oncology agents are likely to serve as useful guides in the discovery, progression, and approval of future drugs or combination of drugs in this class. This review includes summaries of the pharmacokinetics and exposure-response of the immune checkpoint inhibitors approved to date, as well as an overview of some quantitative systems pharmacology approaches. The ability of immuno-oncology to meet its full potential will depend on overcoming development challenges, including the need for clear strategies to determine optimal dose and scheduling for monotherapy as well as combination approaches. Keywords immunopharmacology, oncology, clinical pharmacology, clinical trials, pharmacology, immunotherapy, checkpoint inhibitorsIt could be said that if there were a book chronicling the history and progression of cancer treatment, we are now moving on from the chapter describing the time of sole reliance on chemotherapy, the chapter outlining the advent of targeted therapy is partially completed, and a new chapter on immuno-oncology is just beginning. The past few years have seen the regulatory approval of several immuno-oncology agents (Figure 1), including 6 immune checkpoint inhibitors: ipilimumab (Yervoy; Bristol-Myers Squibb), pembrolizumab (Keytruda; Merck), nivolumab (Opdivo; Bristol-Myers Squibb), atezolizumab (Tecentriq; Genentech), avelumab (Bavencio; EMD Serono), and durvalumab (Imfinzi; AstraZeneca). 1,2Immunotherapy in oncology is a switch from the cell-killing modality using relatively nonspecific cytotoxic methods (chemotherapy) or therapies that target cancer-specific pathways to methods that employ the patients' immune system to attack cancer. The immuno-oncology approach calls on the understanding of complex signaling processes of effector and regulatory...

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“…17 In addition, TPs are typically cleared through nonspecific catabolism or target-mediated elimination, which can be quite different between patients and healthy subjects and at different disease states in patients. 7,17 For TPs with such characteristics, DDI studies need to be conducted in the intended patient population, which makes traditional dedicated DDI study design with intensive PK sampling and balanced treatment arms less feasible.…”

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confidence: 99%

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

Wang

Kassir³

et al. 2017

The Journal of Clinical Pharma

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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.

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

Cited by 16 publications

References 36 publications

Population pharmacokinetics-pharmacodynamics of vedolizumab in patients with ulcerative colitis and Crohn's disease

Population pharmacokinetics-pharmacodynamics of vedolizumab in patients with ulcerative colitis and Crohn's disease

Clinical Pharmacology Considerations for the Development of Immune Checkpoint Inhibitors

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

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