A survey of the way pharmacokinetics are reported in published phase I clinical trials, with an emphasis on oncology

Advancing algorithmic drug product development: Recommendations for machine learning approaches in drug formulation

Artificial intelligence is a rapidly expanding area of research, with the disruptive potential to transform traditional approaches in the pharmaceutical industry, from drug discovery and development to clinical practice. Machine learning, a subfield of artificial intelligence, has fundamentally transformed in silico modelling and has the capacity to streamline clinical translation. This paper reviews data-driven modelling methodologies with a focus on drug formulation development. Despite recent advances, there is limited modelling guidance specific to drug product development and a trend towards suboptimal modelling practices, resulting in models that may not give reliable predictions in practice. There is an overwhelming focus on benchtop experimental outcomes obtained for a specific modelling aim, leaving the capabilities of data scraping or the use of combined modelling approaches yet to be fully explored. Moreover, the preference for high accuracy can lead to a reliance on black box methods over interpretable models. This further limits the widespread adoption of machine learning as black boxes yield models that cannot be easily understood for the purposes of enhancing product performance. In this review, recommendations for conducting machine learning research for drug product development to ensure trustworthiness, transparency, and reliability of the models produced are presented. Finally, possible future directions on how research in this area might develop are discussed to aim for models that provide useful and robust guidance to formulators.

A semi-mechanistic dose-finding design in oncology using pharmacokinetic/pharmacodynamic modeling

While a number of phase I dose-finding designs in oncology exist, the commonly used ones are either algorithmic or empirical model-based. We propose a new framework for modeling the dose-response relationship, by systematically incorporating the pharmacokinetic (PK) data collected in the trial and the hypothesized mechanisms of the drug effects, via dynamic PK/PD modeling, as well as modeling of the relationship between a latent cumulative pharmacologic effect and a binary toxicity outcome. This modeling framework naturally incorporates the information on the impact of dose, schedule and method of administration (e.g., drug formulation and route of administration) on toxicity. The resulting design is an extension of existing designs that make use of pre-specified summary PK information (such as the area under the concentration-time curve [AUC] or maximum serum concentration [Cmax ]). Our simulation studies show, with moderate departure from the hypothesized mechanisms of the drug action, that the performance of the proposed design on average improves upon those of the common designs, including the continual reassessment method (CRM), Bayesian optimal interval (BOIN) design, modified toxicity probability interval (mTPI) method, and a design called PKLOGIT that models the effect of the AUC on toxicity. In case of considerable departure from the underlying drug effect mechanism, the performance of the design is shown to be comparable with that of the other designs. We illustrate the proposed design by applying it to the setting of a phase I trial of a γ-secretase inhibitor in metastatic or locally advanced solid tumors. We also provide R code to implement the proposed design.

The Use of Translational Modelling and Simulation to Develop Immunomodulatory Therapy as an Adjunct to Antibiotic Treatment in the Context of Pneumonia

The treatment of respiratory tract infections is threatened by the emergence of bacterial resistance. Immunomodulatory drugs, which enhance airway innate immune defenses, may improve therapeutic outcome. In this concept paper, we aim to highlight the utility of pharmacometrics and Bayesian inference in the development of immunomodulatory therapeutic agents as an adjunct to antibiotics in the context of pneumonia. For this, two case studies of translational modelling and simulation frameworks are introduced for these types of drugs up to clinical use. First, we evaluate the pharmacokinetic/pharmacodynamic relationship of an experimental combination of amoxicillin and a TLR4 agonist, monophosphoryl lipid A, by developing a pharmacometric model accounting for interaction and potential translation to humans. Capitalizing on this knowledge and associating clinical trial extrapolation and statistical modelling approaches, we then investigate the TLR5 agonist flagellin. The resulting workflow combines expert and prior knowledge on the compound with the in vitro and in vivo data generated during exploratory studies in order to construct high-dimensional models considering the pharmacokinetics and pharmacodynamics of the compound. This workflow can be used to refine preclinical experiments, estimate the best doses for human studies, and create an adaptive knowledge-based design for the next phases of clinical development.

Estimating Similarity of Dose-Response Relationships in Phase I Clinical Trials-Case Study in Bridging Data Package

Bridging studies are designed to fill the gap between two populations in terms of clinical trial data, such as toxicity, efficacy, comorbidities and doses. According to ICH-E5 guidelines, clinical data can be extrapolated from one region to another if dose–reponse curves are similar between two populations. For instance, in Japan, Phase I clinical trials are often repeated due to this physiological/metabolic paradigm: the maximum tolerated dose (MTD) for Japanese patients is assumed to be lower than that for Caucasian patients, but not necessarily for all molecules. Therefore, proposing a statistical tool evaluating the similarity between two populations dose–response curves is of most interest. The aim of our work is to propose several indicators to evaluate the distance and the similarity of dose–toxicity curves and MTD distributions at the end of some of the Phase I trials, conducted on two populations or regions. For this purpose, we extended and adapted the commensurability criterion, initially proposed by Ollier et al. (2019), in the setting of completed phase I clinical trials. We evaluated their performance using three synthetic sets, built as examples, and six case studies found in the literature. Visualization plots and guidelines on the way to interpret the results are proposed.

Pharmacological evidence of medicinal cannabis in oncology: a systematic review

PURPOSE

This systematic literature review examines research into the use of medicinal cannabis in cancer management. The aim was to identify the gaps in knowledge on the dose, dosing schedule and absorption of the administration routes of medicinal cannabis use in oncology.

METHODS

A comprehensive search of the literature was conducted across six databases to identify original data reporting the pharmacology of medicinal cannabis in oncology.

RESULTS

Eighteen articles were selected for review. Of the selected articles, ten were identified as randomised control trials, two experimental studies, two retrospective cohort studies and four case studies. Four articles reported absorption data and one drug interaction study was identified.

CONCLUSIONS

There is little evidence reported in the literature on the absorption of medicinal cannabis in cancer populations. Various reasons are explored for the lack of pharmacokinetic studies for medicinal cannabis in cancer populations, including the availability of assays to accurately assess cannabinoid levels, lack of clinical biomarkers and patient enrolment for pharmacokinetic studies.

dfpk: An R-package for Bayesian dose-finding designs using pharmacokinetics (PK) for phase I clinical trials

BACKGROUND AND OBJECTIVE

Dose-finding, aiming at finding the maximum tolerated dose, and pharmacokinetics studies are the first in human studies in the development process of a new pharmacological treatment. In the literature, to date only few attempts have been made to combine pharmacokinetics and dose-finding and to our knowledge no software implementation is generally available. In previous papers, we proposed several Bayesian adaptive pharmacokinetics-based dose-finding designs in small populations. The objective of this work is to implement these dose-finding methods in an R package, called dfpk.

METHODS

All methods were developed in a sequential Bayesian setting and Bayesian parameter estimation is carried out using the rstan package. All available pharmacokinetics and toxicity data are used to suggest the dose of the next cohort with a constraint regarding the probability of toxicity. Stopping rules are also considered for each method. The ggplot2 package is used to create summary plots of toxicities or concentration curves.

RESULTS

For all implemented methods, dfpk provides a function (nextDose) to estimate the probability of efficacy and to suggest the dose to give to the next cohort, and a function to run trial simulations to design a trial (nsim). The sim.data function generates at each dose the toxicity value related to a pharmacokinetic measure of exposure, the AUC, with an underlying pharmacokinetic one compartmental model with linear absorption. It is included as an example since similar data-frames can be generated directly by the user and passed to nsim.

CONCLUSION

The developed user-friendly R package dfpk, available on the CRAN repository, supports the design of innovative dose-finding studies using PK information.

Dose-finding methods for Phase I clinical trials using pharmacokinetics in small populations

The aim of phase I clinical trials is to obtain reliable information on safety, tolerability, pharmacokinetics (PK), and mechanism of action of drugs with the objective of determining the maximum tolerated dose (MTD). In most phase I studies, dose‐finding and PK analysis are done separately and no attempt is made to combine them during dose allocation. In cases such as rare diseases, paediatrics, and studies in a biomarker‐defined subgroup of a defined population, the available population size will limit the number of possible clinical trials that can be conducted. Combining dose‐finding and PK analyses to allow better estimation of the dose‐toxicity curve should then be considered. In this work, we propose, study, and compare methods to incorporate PK measures in the dose allocation process during a phase I clinical trial. These methods do this in different ways, including using PK observations as a covariate, as the dependent variable or in a hierarchical model. We conducted a large simulation study that showed that adding PK measurements as a covariate only does not improve the efficiency of dose‐finding trials either in terms of the number of observed dose limiting toxicities or the probability of correct dose selection. However, incorporating PK measures does allow better estimation of the dose‐toxicity curve while maintaining the performance in terms of MTD selection compared to dose‐finding designs that do not incorporate PK information. In conclusion, using PK information in the dose allocation process enriches the knowledge of the dose‐toxicity relationship, facilitating better dose recommendation for subsequent trials.

The changing landscape of phase I trials in oncology

KEY POINTS

Several aspects of phase I trials have evolved in the current era of molecular targeted agents to adapt to the changing nature of anticancer therapy and to increase the efficiency of drug development.

Current phase I designs are increasingly integrating novel dose-escalation approaches and biomarker-driven selection of patients, as well as expanding study objectives to include the evaluation of efficacy and pharmacodynamics/pharmacokinetics in addition to safety.

Changes to the regulatory approval process have helped to expedite drug development, particularly for novel agents with a strong biologic rationale and proof of concept, validated predictive biomarker, and clear evidence of efficacy in early trials.

As a result of the substantial changes in phase I trial goals and conduct, there is a parallel shift toward multi-institutional trials and central study management by clinical research organizations.

The use of multi-institutional trials has a significant impact on the structure of phase I programs and the experience of investigators, particularly because of limited patient enrollment at each site.

Dose Response and Pharmacokinetics of Tofacitinib (CP-690,550), an Oral Janus Kinase Inhibitor, in the Treatment of Chronic Plaque Psoriasis

Longitudinal nonlinear mixed effects modeling was used to characterize the dose–response profile of tofacitinib using data from a placebo-controlled dose-ranging study, where tofacitinib 2, 5, and 15 mg twice daily (b.i.d.) were evaluated for plaque psoriasis treatment. Bayesian estimation was applied with prior information derived from the literature: nonclinical and clinical data in psoriasis, as well as other indications. The probability to achieve a certain target effect associated with a given dose was calculated from the posterior samples. On the basis of these probabilities along with safety considerations, tofacitinib 5 and 10 mg b.i.d. were selected for further testing in confirmatory phase III clinical trials. Pharmacokinetics in patients with psoriasis was characterized using a population-based modeling approach, and body weight was identified as an important covariate. A subgroup analysis suggested reduced efficacy of tofacitinib with increasing body weight; however, it is unclear whether this trend could be explained by systemic exposure alone.

An overview of the optimal planning, design, and conduct of phase I studies of new therapeutics

Phase I clinical trials represent the first step in bringing promising new treatments from the laboratory to the clinic. Although the importance of phase I clinical trials is widely recognized, there is currently no consensus among the scientific, medical, and statistical communities on how best to do these studies in humans. With the advent of targeted therapies, it has become evident that we need to tailor the design of phase I studies for the particular drug class under investigation and any endpoints that are being defined. The National Cancer Institute (NCI) Investigational Drug Steering Committee (IDSC) provides broad external scientific and clinical input on the design and prioritization of early-phase clinical trials with agents for which the NCI Cancer Therapy Evaluation Program (CTEP) holds an Investigational New Drug (IND) application through the U.S. Food and Drug Administration (FDA). The IDSC has formed a number of task forces and working groups, including the Clinical Trial Design Task Force and the Biomarker Working Group, many with membership from within the IDSC as well as external experts, including participants from academia, the pharmaceutical industry, and regulatory authorities. The Clinical Trials Design Taskforce sponsored a Phase I Workshop with the primary goal being to develop consensus recommendations for the optimal design of phase I studies. The primary focus included (1) efficient trial designs, (2) phase I drug combinations, and (3) appropriate statistical and correlative endpoints. In this CCR Focus series, articles summarize key aspects and recommendations on phase I studies (including combination trials), such as design, use of biomarkers, the European Union and Japanese perspectives on design, requirements for first-in-human and other phase I studies, and ensuring regulatory and International Conference on Harmonization (ICH) compliance. A final article summarizes recommendations for the design and conduct of phase II studies.