The estimation and translation uncertainties in applying NOAEL to clinical dose escalation.

The systemic exposure at the no-observed-adverse-effect-level (NOAEL) estimated from animals is an important criterion commonly applied to guard the safety of participants in clinical trials of investigational drugs. However, the discrepancy in toxicity profile between species is widely recognized. The objective of the work reported here was to assess, via simulation, the level of uncertainty in the NOAEL estimated from an animal species and the effectiveness of applying its associated exposure value to minimizing the toxicity risk to human. Simulations were conducted for dose escalation of an investigational new chemical entity with hypothetical exposure-response models for the dose-limiting toxicity under a variety of conditions, in terms of between-species relative sensitivity to the toxicity and the between-subject variability in the key parameters determining the sensitivity and pharmacokinetics. Results show a high uncertainty in the NOAEL estimation. Notably, even when the animal species and humans are assumed to have the same sensitivity, which may not be realistic, limiting clinical dose to the exposure at the NOAEL that has been identified in the animals carries a high risk of either causing toxicity or under-dosing, hence undermining the therapeutic potential of the drug candidate. These findings highlight the importance of understanding the mechanism of the toxicity profile and its cross-species translatability, as well as the importance of understanding the dose requirement for achieving adequate pharmacology.

Concept and application of the probability of pharmacological success (PoPS) as a decision tool in drug development: a position paper.

BACKGROUND: In drug development, few molecules from a large pool of early candidates become successful medicines after demonstrating a favourable benefit-risk ratio. Many decisions are made along the way to continue or stop the development of a molecule. The probability of pharmacological success, or PoPS, is a tool for informing early-stage decisions based on benefit and risk data available at the time. RESULTS: The PoPS is the probability that most patients can achieve adequate pharmacology for the intended indication while minimising the number of subjects exposed to safety risk. This probability is usually a function of dose; hence its computation typically requires exposure-response models for pharmacology and safety. The levels of adequate pharmacology and acceptable risk must be specified. The uncertainties in these levels, in the exposure-response relationships, and in relevant translation all need to be identified. Several examples of different indications are used to illustrate how this approach can facilitate molecule progression decisions for preclinical and early clinical development. The examples show that PoPS assessment is an effective mechanism for integrating multi-source data, identifying knowledge gaps, and forcing transparency of assumptions. With its application, translational modelling becomes more meaningful and dose prediction more rigorous. Its successful implementation calls for early planning, sound understanding of the disease-drug system, and cross-discipline collaboration. Furthermore, the PoPS evolves as relevant knowledge grows. CONCLUSION: The PoPS is a powerful evidence-based framework to formally capture multiple uncertainties into a single probability term for assessing benefit-risk ratio. In GSK, it is now expected for governance review at all early-phase decision gates.

The area under the effect curve as an efficacy determinant for anti-infectives.

Pharmacokinetic/pharmacodynamic (PK/PD) indices making use of area under the curve, maximum concentration, and the duration that in vivo drug concentration is maintained above a critical level are commonly applied to clinical dose prediction from animal efficacy experiments in the infectious disease arena. These indices make suboptimal use of the nonclinical data, and the prediction depends on the shape of the PK profiles in the animals, determined by the species-specific absorption, distribution, metabolism, and elimination properties, and the dosing regimen used in the efficacy experiments. Motivated by the principle that efficacy is driven by pharmacology, we conducted simulations using a generalized pathogen dynamic model, to assess the properties of an alternative efficacy predictor: the area under the effect curve (AUEC), computed using in vitro PD and in vivo PK. Across a wide range of hypothetical scenarios, the AUEC consistently showed regimen-independent strong correlation (R2 0.76-0.98) with in vivo efficacy, superior to all other indices. These findings serve as proof of concept that AUEC should be considered in practice as a translation tool for cross-species dose prediction. Using AUEC for clinical dose prediction could also potentially cut down animal use by reducing or avoiding dose fractionation experiments.

Systemic Exposure of Rituximab Increased by Ibrutinib: Pharmacokinetic Results and Modeling Based on the HELIOS Trial.

INTRODUCTION: In the HELIOS trial, bendamustine/rituximab (BR) plus ibrutinib (BR-I) improved disease outcomes versus BR plus placebo in previously treated chronic lymphocytic leukemia/small lymphocytic lymphoma. Here, we describe the pharmacokinetic (PK) observations, along with modeling to further explore the interaction between ibrutinib and rituximab. METHODS: 578 subjects were randomized to ibrutinib or placebo with BR (6 cycles). Ibrutinib PK samples and tumor measurements were obtained from all subjects; a subset was evaluated for bendamustine and rituximab PK. Population rituximab PK was assessed using nonlinear mixed-effects modeling. RESULTS: Dose-normalized plasma concentration-time bendamustine data were comparable between the arms. Systemic rituximab exposure was higher with BR-I versus BR; mean trough serum concentrations were 2- to 3-fold higher in the first three cycles and 1.2- to 1.7-fold higher subsequently. No relevant safety differences were observed. In the modeling, including treatment arm as a categorical covariate and tumor burden as a continuous time-varying covariate on overall rituximab clearance significantly improved fitting of the data. CONCLUSIONS: BR-I led to higher dose-normalized systemic rituximab exposure versus BR and more rapid steady-state achievement. The modeling data suggest that rituximab disposition is, at least in part, target mediated. Determining the clinical significance of these findings requires further assessments. TRIAL REGISTRATION: This study is registered at https://clinicaltrials.gov/ct2/show/NCT01611090 .

MPBPK-TMDD models for mAbs: alternative models, comparison, and identifiability issues.

The aim of the present study was to evaluate model identifiability when minimal physiologically-based pharmacokinetic (mPBPK) models are integrated with target mediated drug disposition (TMDD) models in the tissue compartment. Three quasi-steady-state (QSS) approximations of TMDD dynamics were explored: on (a) antibody-target complex, (b) free target, and (c) free antibody concentrations in tissue. The effects of the QSS approximations were assessed via simulations, taking as reference the mPBPK-TMDD model with no simplifications. Approximation (a) did not affect model-derived concentrations, while with the inclusion of approximation (b) or (c), target concentration profiles alone, or both drug and target concentration profiles respectively deviated from the reference model profiles. A local sensitivity analysis was performed, highlighting the potential importance of sampling in the terminal pharmacokinetic phase and of collecting target concentration data. The a priori and a posteriori identifiability of the mPBPK-TMDD models were investigated under different experimental scenarios and designs. The reference model and QSS approximation (a) on antibody-target complex were both found to be a priori identifiable in all scenarios, while under the further inclusion of QSS approximation (b) target concentration data were needed for a priori identifiability to be preserved. The property could not be assessed for the model including all three QSS approximations. A posteriori identifiability issues were detected for all models, although improvement was observed when appropriate sampling and dose range were selected. In conclusion, this work provides a theoretical framework for the assessment of key properties of mathematical models before their experimental application. Attention should be paid when applying integrated mPBPK-TMDD models, as identifiability issues do exist, especially when rich study designs are not feasible.

Mathematical modeling of efficacy and safety for anticancer drugs clinical development.

INTRODUCTION: Drug attrition in oncology clinical development is higher than in other therapeutic areas. In this context, pharmacometric modeling represents a useful tool to explore drug efficacy in earlier phases of clinical development, anticipating overall survival using quantitative model-based metrics. Furthermore, modeling approaches can be used to characterize earlier the safety and tolerability profile of drug candidates, and, thus, the risk-benefit ratio and the therapeutic index, supporting the design of optimal treatment regimens and accelerating the whole process of clinical drug development. Areas covered: Herein, the most relevant mathematical models used in clinical anticancer drug development during the last decade are described. Less recent models were considered in the review if they represent a standard for the analysis of certain types of efficacy or safety measures. Expert opinion: Several mathematical models have been proposed to predict overall survival from earlier endpoints and validate their surrogacy in demonstrating drug efficacy in place of overall survival. An increasing number of mathematical models have also been developed to describe the safety findings. Modeling has been extensively used in anticancer drug development to individualize dosing strategies based on patient characteristics, and design optimal dosing regimens balancing efficacy and safety.

Current mathematical models for cancer drug discovery.

INTRODUCTION: Pharmacometric models represent the most comprehensive approaches for extracting, summarizing and integrating information obtained in the often sparse, limited, and less-than-optimally designed experiments performed in the early phases of oncology drug discovery. Whilst empirical methodologies may be enough for screening and ranking candidate drugs, modeling approaches are needed for optimizing and making economically viable the learn-confirm cycles within an oncology research program and anticipating the dose regimens to be investigated in the subsequent clinical development. Areas covered: Papers appearing in the literature of approximately the last decade reporting modeling approaches applicable to anticancer drug discovery have been listed and commented. Papers were selected based on the interest in the proposed methodology or in its application. Expert opinion: The number of modeling approaches used in the discovery of anticancer drugs is consistently increasing and new models are developed based on the current directions of research of new candidate drugs. These approaches have contributed to a better understanding of new oncological targets and have allowed for the exploitation of the relatively sparse information generated by preclinical experiments. In addition, they are used in translational approaches for guiding and supporting the choice of dosing regimens in early clinical development.