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

Therapeutic Protein Drug Interactions: A White Paper From the International Consortium for Innovation and Quality in Pharmaceutical Development

Typically, therapeutic proteins (TPs) have a low risk for eliciting meaningful drug interactions (DIs). However, there are select instances where TP drug interactions (TP-DIs) of clinical concern can occur. This white paper discusses the various types of TP-DIs involving mechanisms such as changes in disease state, target-mediated drug disposition, neonatal Fc receptor (FcRn), or antidrug antibodies formation. The nature of TP drug interaction being investigated should determine whether the examination is conducted as a standalone TP-DI study in healthy participants, in patients, or assessed via population pharmacokinetic analysis. DIs involving antibody-drug conjugates are discussed briefly, but the primary focus here will be DIs involving cytokine modulation. Cytokine modulation can occur directly by certain TPs, or indirectly due to moderate to severe inflammation, infection, or injury. Disease states that have been shown to result in indirect disease-DIs that are clinically meaningful have been listed (i.e., typically a twofold change in the systemic exposure of a coadministered sensitive cytochrome P450 substrate drug). Type of disease and severity of inflammation should be the primary drivers for risk assessment for disease-DIs. While more clinical inflammatory marker data needs to be collected, the use of two or more clinical inflammatory markers (such as C-reactive protein, albumin, or interleukin 6) may help broadly categorize whether the predicted magnitude of inflammatory disease-DI risk is negligible, weak, or moderate to strong. Based on current knowledge, clinical DI studies are not necessary for all TPs, and should no longer be conducted in certain disease patient populations such as psoriasis, which do not have sufficient systemic inflammation to cause a meaningful indirect disease-DI.

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.

Therapeutic protein-drug interactions: plausible mechanisms and assessment strategies

INTRODUCTION

Over the last three decades, therapeutic proteins have played an increasingly important role in pharmacotherapy. Owing to an expected significant increase in the coadministration of biotherapeutics with established pharmacotherapy regimens or even with other biotherapeutic agents, there is an increasing likelihood for the occurrence of clinically relevant drug interactions, so called therapeutic protein-drug interactions (TP-DIs). Areas covered: Our current understanding of TP-DIs and recent collaborations among industry, academia and regulatory agencies are reviewed in this article. Although most of the observed TP-DIs are mediated by disease states, immune status, and/or target physiology, TP-DI assessments are still done empirically. Plausible mechanisms of major TP-DIs involving therapeutic proteins (primarily monoclonal antibodies), either as victims or as perpetrators, are proposed, with mechanism-based strategies and assessment approaches to better evaluate their propensity are recommended. Expert opinion: Our current understanding of the mechanisms of TP-DIs is in its infancy. Much of the basic research needs to be conducted to verify existing TP-DI hypotheses or help predict and manage potential ones, whose efforts are not considered trivial and may be better achieved through close collaborations among scientists from academia, industry, and regulatory agencies.

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.

Immune Checkpoint inhibitors: An introduction to the next-generation cancer immunotherapy

Activating the immune system to eliminate cancer cells and produce clinically relevant responses has been a long-standing goal of cancer research. Most promising therapeutic approaches to activating antitumor immunity include immune checkpoint inhibitors. Immune checkpoints are numerous inhibitory pathways hardwired in the immune system. They are critical for maintaining self-tolerance and modulating the duration and amplitude of physiological immune responses in peripheral tissues to minimize collateral tissue damage. Tumors regulate certain immune checkpoint pathways as a major mechanism of immune resistance. Because immune checkpoints are initiated by ligand-receptor interactions, blockade by antibodies provides a rational therapeutic approach. Although targeted therapies are clinically successful, they are often short-lived due to rapid development of resistance. Immunotherapies offer one notable advantage. Enhancing the cell-mediated immune response against tumor cells leads to generation of a long-term memory lymphocyte population patrolling the body to attack growth of any new tumor cells, thereby sustaining the therapeutic effects. Furthermore, early clinical results suggest that combination immunotherapies offer even more potent antitumor activity. This review is intended to provide an introduction to immune checkpoint inhibitors and discusses the scientific overview of cancer immunotherapy, mechanisms of the inhibitors, clinical pharmacology considerations, advances in combination therapies, and challenges in drug development.

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

BACKGROUND

Vedolizumab, an anti-α(4)β(7) 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.

AIMS

To 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.

METHODS

Data 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.

RESULTS

Vedolizumab 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 (CL(L)) 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 CL(L).

CONCLUSIONS

Population 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).

Pharmacokinetic drug-drug interaction assessment of peptibody trebananib in combination with chemotherapies

PURPOSE

To provide the first evaluation of pharmacokinetic (PK) drug-drug interactions (DDIs) between trebananib and chemotherapies across tumor types.

METHODS

PK data of trebananib and chemotherapies (paclitaxel, carboplatin, pegylated liposomal doxorubicin, topotecan, capecitabine, lapatinib, 5-FU, irinotecan, or docetaxel) were collected from trials of ovarian cancer, metastatic breast cancer, colorectal carcinoma, and mixed solid tumor. A dedicated PK DDI study of trebananib and paclitaxel in patients with mixed solid tumors was also conducted. The geometric least squares mean (GLSM) ratios and corresponding 90 % confidence intervals (CI) of C max and AUC were estimated for DDI evaluations.

RESULTS

In the PK DDI study of trebananib and paclitaxel, the GLSM ratio (90 % CI) was 1.17 (1.10-1.25) for paclitaxel AUC and 1.30 (1.15-1.48) for paclitaxel C max. The GLSM ratio (90 % CI) for the effect of paclitaxel on trebananib PK was 0.92 (0.87-0.97) for trebananib AUC and 0.98 (0.92-1.05) for trebananib C max. In the remaining studies, the GLSM ratios (90 % CI) of C max and AUC generally ranged from 0.8 to 1.25 or exhibited less than twofold PK variabilities across chemotherapeutic agents. No dose-dependent DDIs were evident.

CONCLUSIONS

No PK DDI was deemed clinically meaningful between trebananib and the tested chemotherapeutic agents to warrant dose adjustments.