Towards a better prediction of peak concentration, volume of distribution and half-life after oral drug administration in man, using allometry

Comparison of Various Approaches to Translate Non-Linear Pharmacokinetics of Monoclonal Antibodies from Cynomolgus Monkey to Human

BACKGROUND AND OBJECTIVES

The prediction of pharmacokinetics of monoclonal antibodies (mAbs) exhibiting non-linear pharmacokinetics in preclinical species to human is challenging, and very limited scientific work has been published in this field of research. Therefore, we have conducted an elaborate comparative assessment to determine the most reliable preclinical to clinical scaling strategy for mAbs with non-linear pharmacokinetics.

METHODS

We have compared three different scaling approaches to predict human pharmacokinetics from cynomolgus monkey. In the first approach, cynomolgus monkey pharmacokinetic parameters estimated using a two-compartment model with parallel linear and non-linear elimination were allometrically scaled to simulate human pharmacokinetics. In the second approach, allometric exponents were integrated with a minimal physiologically based pharmacokinetic (mPBPK) model to translate human pharmacokinetics. In the third approach, we have employed a species time-invariant method, wherein a two-compartment model with parallel linear and non-linear elimination was used as a framework model for simulation of the human profile.

RESULTS

Human exposure parameters projected by an integrated allometric method were only within two fold for approximately 45-70% of predictions at different doses of five mAbs evaluated, while approximately 70-80% of Cmax and AUC predictions by integrated mPBPK modelling as well as the species time-invariant method were within two-fold error. The average fold error for clearance predictions by the integrated mPBPK method was 1.10-1.45 fold, whilst for the species time-variant and integrated allometric methods, the average fold error was between 1.04 and 1.37 fold and 1.24 and 2.13 fold, respectively.

CONCLUSIONS

Our findings suggest that the species time-variant method and mPBPK proposed by us can be employed to reliably translate non-linear pharmacokinetics of mAbs from cynomolgus monkey to human.

First-in-human dose: current status review for better future perspectives

AIM

The aim of this article is to understand the pros and cons of various methods involved in first-in-human (FIH) dose calculation and act decisively in dose escalations when calculating the maximum tolerated dose.

SUBJECTS AND METHODS

We reviewed early phase clinical trials for methods of FIH dose and dose-escalation steps and discuss them in line with existing guidelines. We also reviewed the clinical trial registry to recognize trends in trial registration in recent years and after a massive failure in a few trials.

RESULTS

Phase 1 trials of TGN 1412 and BIA10-2474 would always be remembered as catastrophes for pharmaceutical development plans. Quite often than not, healthy human volunteers are the guinea pigs in this stage of drug development. And, the most important aspect of designing an early phase study is deciding upon the dose to be started with, apart from the selection of cohort and escalation steps. The common principles used for FIH dose calculation include no observed adverse effect level, minimum anticipated biological effect level, pharmacologically active dose, pharmacokinetic/pharmacodynamic approach, and similar drug comparison approach.

CONCLUSION

Early phase clinical trials are basically foundation stones on which lies the entire onus of the later stages of development. Deciding FIH dose is a crucial step that necessitates the incorporation of detailed data from the preclinical stages and application of the most conservative approach for the safety/benefit of the volunteers in these studies.

First dose in neonates: pharmacokinetic bridging study from juvenile mice to neonates for drugs metabolized by CYP3A

First dose prediction is challenging in neonates. Our objective in this proof-of-concept study was to perform a pharmacokinetic (PK) bridging study from juvenile mice to neonates for drugs metabolized by CYP3A. We selected midazolam and clindamycin as model drugs. We developed juvenile mice population PK models using NONMEM. The PK parameters of these two drugs in juvenile mice were used to bridge PK parameters in neonates using different correction methods. The bridging results were evaluated by the fold-error of 0.5- to 1.5-fold. Simple allometry with and without a correction factor for maximum lifespan potential could be used for a bridging of clearance (CL) and volume of distribution (V_d), respectively, from juvenile mice to neonates. Simulation results demonstrated that for midazolam, 100% of clinical studies for which both the predictive CL and V_d were within 0.5- to 1.5-fold of the observed. For clindamycin, 75% and 100% of clinical studies for which the predictive CL and V_d were within 0.5- to 1.5-fold of the observed. A PK bridging of drugs metabolized by CYP3A is feasible from juvenile mice to neonates. It could be a complement to the ADE and PBPK models to support the first dose in neonates.

Prediction of active human dose: learnings from 20 years of Merck KGaA experience, illustrated by case studies

High-quality dose predictions based on a good understanding of target engagement is one of the main translational goals in drug development. Here, we systematically evaluate active human dose predictions for 15 Merck KGaA/EMD Serono assets spanning several modalities and therapeutic areas. Using case studies, we illustrate the value of adhering to the translational best practices of having an exposure-response relationship in an appropriate animal model; having validated, translatable pharmacodynamic (PD) biomarkers measurable in Phase I populations in the right tissue; having a deeper understanding of biology; and capturing uncertainties in predictions. Given the gap in publications on the subject, we believe that the learnings from this unique diverse data set, which are generic to the industry, will trigger actions to improve future predictions.

Biopharmaceutical characters and bioavailability improving strategies of ginsenosides

Deglycosylation is the most important gastrointestinal metabolism in which ginsenosides are split off from glycosyl moieties by the enzymes secreted from intestinal microflora, and two possible metabolic pathways of protopanaxdiol-type ginsenosides (PPD-type ginsenosides) and protopanaxtriol-type ginsenosides (PPT-type ginsenosides) have been concluded. The former is deglycosylated at C-3 and/or C-20, and transformed to protopanaxdiol (PPD). By comparison, the latter is deglycosylated at C-6 and/or C-20, and eventually transformed to protopanaxtriol (PPT) instead. The pharmacokinetic behavior of PPD-type ginsenosides and PPT-type ginsenosides is different, mainly in a faster absorption and elimination rate of PPT-type ginsenosides, but almost all of ginsenosides have a low oral bioavailability, which is relevant to the properties, the stability in the gastrointestinal tract, membrane permeability and the intestinal and hepatic first-pass effect of ginsenosides. Fortunately, its bioavailability can be improved by means of pharmaceutical strategies, including nanoparticles, liposomes, emulsions, micelles, etc. These drug delivery systems can significantly increase the bioavailability of ginsenosides, as well as controlling or targeting drug release. Ginsenosides are widely used in the treatment of various diseases, the most famous one is the Shen Yi capsule, which is the world's first clinical application of tumor neovascularization inhibitors. Hence, this article aims to draw people's attention on ocotillol-type ginsenosides, which have prominent anti-Alzheimer's disease activity, but have been overlooked previously, such as its representative compound-Pseudoginsenoside F11(PF11), and then provide a reference for the druggability and further developments of ocotillol-type ginsenosides by utilizing the homogeneous structure between dammarane-type ginsenosides and ocotillol-type ginsenosides.

First dose in neonates: are juvenile mice, adults and in vitro-in silico data predictive of neonatal pharmacokinetics of fluconazole

BACKGROUND AND OBJECTIVES

Selection of the first-dose-in-neonates is challenging. The objective of this proof-of-concept study was to evaluate a pharmacokinetic bridging approach to predict a neonatal dosing regimen.

METHODS

We selected fluconazole as a paradigm compound. We used data from studies in juvenile mice and adults to develop population pharmacokinetic models using NONMEM. We also develop a physiologically-based pharmacokinetic model from in vitro-in silico data using Simcyp. These three models were then used to predict neonatal pharmacokinetics and dosing regimens for fluconazole.

RESULTS

From juvenile mice to neonates, a correction factor of maximum lifespan potential should be used for extrapolation, while a "renal factor" taking into account renal maturation was required for successful bridging based on adult and in vitro-in silico data. Simulations results demonstrated that the predicted drug exposure based on bridging approach was comparable to the observed value in neonates. The prediction errors were -2.2, +10.1 and -4.6 % for juvenile mice, adults and in vitro-in silico data, respectively.

CONCLUSION

A model-based bridging approach provided consistent predictions of fluconazole pharmacokinetic parameters in neonates and demonstrated the feasibility of this approach to justify the first-dose-in-neonates, based on all data available from different sources (including physiological informations, preclinical studies and adult data), allowing evidence-based decisions of neonatal dose rather than empiricism.

Tools for predicting the PK/PD of therapeutic proteins

INTRODUCTION

Assessments of the pharmacokinetic/pharmacodynamic (PK/PD) characteristics are an integral part in the development of novel therapeutic agents. Compared with traditional small molecule drugs, therapeutic proteins possess many distinct PK/PD features that necessitate the application of modified or separate approaches for assessing their PK/PD relationships.

AREAS COVERED

In this review, the authors discuss tools that are utilized to describe and predict the PK/PD features of therapeutic proteins and that are valuable additions in the armamentarium of drug development approaches to facilitate and accelerate their successful preclinical and clinical development.

EXPERT OPINION

A variety of state-of-the-art PK/PD tools is currently being applied and has been adjusted to support the development of proteins as therapeutics, including allometric scaling approaches, target-mediated disposition models, first-in-man dose calculations, physiologically based PK models and empirical and semi-mechanistic PK/PD modeling. With the advent of the next generation of biologics including bioengineered antibody constructs being developed, these tools will need to be further refined and adapted to ensure their applicability and successful facilitation of the drug development process for these novel scaffolds.

Whole-exome sequencing identifies novel LEPR mutations in individuals with severe early onset obesity

OBJECTIVE

Obesity is a major public health problem that increases the risk for a broad spectrum of co-morbid conditions. Despite evidence for a strong genetic contribution to susceptibility to obesity, previous efforts to discover the relevant genes using positional cloning have failed to account for most of the apparent genetic risk variance.

DESIGN AND METHODS

Deploying a strategy combining analysis of exome sequencing data in extremely obese members of four consanguineous families with segregation analysis, we screened for causal genetic variants. Filter-based analysis and homozygosity mapping were used to identify and prioritize putative functional variants.

RESULTS

Two novel frameshift mutations in the leptin receptor in two of the families were identified.

CONCLUSIONS

These results provide proof-of-principle that whole-exome sequencing of families segregating for extreme obesity can identify causal pathogenic mutations. The methods described here can be extended to additional families segregating for extreme obesity and should enable the identification of mutations in novel genes that predispose to obesity.

Interspecies modeling and prediction of human exenatide pharmacokinetics

PURPOSE

To develop a model-based approach for interspecies scaling of the preclinical pharmacokinetics of exenatide and to predict concentration-time profiles in humans.

METHODS

A target-mediated drug disposition (TMDD) model was simultaneously fit to concentration-time profiles of exenatide over a wide range of intravenous (IV) and subcutaneous (SC) doses obtained from mice, rats, and monkeys. Allometric relationships were incorporated into the model to scale parameters based on species body weight. Human pharmacokinetic profiles following IV and SC administration were simulated using the final model structure and parameter estimates and compared to clinical data.

RESULTS

The final model provided a good simultaneous fit to all animal data and reasonable parameter estimates. Exenatide receptor binding affinity and baseline receptor concentrations were species-dependent. Absorption parameters from rat provided the best prediction of exenatide SC absorption in humans, but good predictions could also be obtained using allometric scaling of preclinical absorption parameters.

CONCLUSIONS

A TMDD model combined with allometric scaling was successfully used to simultaneously describe preclinical data for exenatide from three animal species following both IV and SC administration. The majority of model parameters could be shared among the animal species and further used for projecting exenatide behavior in humans.

Prediction of human oral plasma concentration-time profiles using preclinical data: comparative evaluation of prediction approaches in early pharmaceutical discovery

BACKGROUND AND OBJECTIVES

Empirically based methods remain one of our tools in human pharmacokinetic predictions. The Dedrick approach and the steady-state plasma drug concentration (C(ss))-mean residence time (MRT) approach are based on the assumption that concentration-time profiles are similar among species, including man, and that curves derived from a variety of animal species can be superimposed after mathematical transformation. In the Dedrick approach the transformation is based on the slope and intercept of the allometric relationship. The C(ss)-MRT approach is based on the implementation of measured animal and predicted human MRT and dose/volume of distribution at steady state (V(ss)). The aims of the present study were to compare the predictive performance of concentration-time profiles obtained by these approaches, to evaluate the prediction of individual pharmacokinetic parameters by these approaches and to further refine these approaches incorporating the experience from our previous work.

METHODS

A retrospective analysis using 35 proprietary compounds developed at Johnson & Johnson Pharmaceutical Research and Development was conducted to compare the accuracies of the Dedrick and C(ss)-MRT approaches for predicting oral concentration-time profiles and pharmacokinetic parameters in man. In the first step, input for the transformation was based on simple allometry. Then we assessed whether both methods could be fine-tuned by systematically incorporating correction factors (maximum life span potential, brain weight and plasma protein binding), depending on the interspecies relationship. In addition, for the C(ss)-MRT approach, we used formulas based on multivariate regression analysis as input for the transformation.

RESULTS

Inclusion of correction factors significantly improved the profile predictability for the Dedrick and C(ss)-MRT approaches. This was mainly linked to an improved prediction of terminal elimination half-life (t(½)), MRT and the ratio between the maximum plasma concentration and the concentration at the last observed time point (C(max)/C(last)). No significant differences were observed between the Dedrick approach with correction factors, the C(ss)-MRT approach with correction factors and the C(ss)-MRT approach, based on the regression equations.

CONCLUSIONS

Based on the dataset evaluated in this study, we demonstrated that human plasma concentration-time profiles and pharmacokinetic parameters could be predicted with the Dedrick and C(ss)-MRT approaches and that if correction factors were implemented, the predictions improved significantly. With the requirement of only a limited preclinical in vivo pharmacokinetic dataset, these empirical methods could offer potential in the early stages of drug discovery.