Automated proper lumping for simplification of linear physiologically based pharmacokinetic systems Abstract Physiologically based pharmacokinetic (PBPK) models are an important type of systems model used commonly in drug development before commencement of first-in-human studies. Due to structural complexity, these models are not easily utilised for future data-driven population pharmacokinetic (PK) analyses that require simpler models. In the current study we aimed to explore and automate methods of simplifying PBPK models using a proper lumping technique. A linear 17-state PBPK model for fentanyl was identified from the literature. Four methods were developed to search the optimal lumped model, including full enumeration (the reference method), non-adaptive random search (NARS), scree plot plus NARS, and simulated annealing (SA). For exploratory purposes, it was required that the total area under the fentanyl arterial concentration–time curve (AUC) between the lumped and original models differ by 0.002% at maximum. In full enumeration, a 4-state lumped model satisfying the exploratory criterion was found. In NARS, a lumped model with the same number of lumped states was found, requiring a large number of random samples. The scree plot provided a starting lumped model to NARS and the search completed within a short time. In SA, a 4-state lumped model was consistently delivered. In simplify an existing linear fentanyl PBPK model, SA was found to be robust and the most efficient and may be suitable for general application to other larger-scale linear systems. Ultimately, simplified PBPK systems with fundamental mechanisms may be readily used for data-driven PK analyses.

Computer-assembled cross-species/cross-modalities two-pore physiologically based pharmacokinetic model for biologics in mice and rats Abstract Two-pore physiologically-based pharmacokinetic (PBPK) models can be expected to describe the tissue distribution and elimination kinetics of soluble proteins, endogenous or dosed, as function of their size. In this work, we amalgamated our previous two-pore PBPK model for an inert domain antibody (dAb) in mice with the cross-species platform PBPK model for monoclonal antibodies described in literature into a unified two-pore platform that describes protein modalities of different sizes and includes neonatal Fc receptor (FcRn) mediated recycling. This unified PBPK model was parametrized for organ-specific lymph flow rates and the endosomal recycling rate constant using an extended tissue distribution time-course dataset that included an inert dAb, albumin and IgG in rats and mice. The model was evaluated by comparing the ab initio predictions for the tissue distribution and elimination properties of albumin-binding dAbs (AlbudAbsTM) in mice and rats with the experimental observations. Due to the large number of molecular species and reactions involved in large-scale PBPK models, we have also developed and deployed a MatlabTM script for automating the assembly of SimBiologyTM-based two-pore biologics PBPK models which drastically cuts the time and effort required for model building.

Bayesian approach to investigate a two-state mixed model of COPD exacerbations Abstract Chronic obstructive pulmonary disease (COPD) is a chronic obstructive disease of the airways. An exacerbation of COPD is defined as shortness of breath, cough, and sputum production. New therapies for COPD exacerbations are examined in clinical trials frequently based on the number of exacerbations that implies long-term study due to the high variability in occurrence and duration of the events. In this work, we expanded the two-state model developed by Cook et al. where the patient transits from an asymptomatic (state 1) to a symptomatic state (state 2) and vice versa, through investigating different semi-Markov models in a Bayesian context using data from actual clinical trials. Of the four models tested, the log-logistic model was shown to adequately characterize the duration and number of COPD exacerbations. The patient disease stage was found a significant covariate with an effect of accelerating the transition from asymptomatic to symptomatic state. In addition, the best dropout model (log-logistic) was incorporated in the final two-state model to describe the dropout mechanism. Simulation based diagnostics such as posterior predictive check (PPC) and visual predictive check (VPC) were used to assess the behaviour of the model. The final model was applied in three clinical trial data to investigate its ability to detect the drug effect: the drug effect was captured in all three datasets and in both directions (from state 1 to state 2 and vice versa). A practical design investigation was also carried out and showed the limits of reducing the number of subjects and study length on the drug effect identification. Finally, clinical trial simulation confirmed that the model can potentially be used to predict medium term (6–12 months) clinical trial outcome using the first 3 months data, but at the expense of showing a non-significant drug effect.

A translational platform PBPK model for antibody disposition in the brain Abstract In this manuscript, we have presented the development of a novel platform physiologically-based pharmacokinetic (PBPK) model to characterize brain disposition of mAbs in the mouse, rat, monkey and human. The model accounts for known anatomy and physiology of the brain, including the presence of distinct blood–brain barrier and blood–cerebrospinal fluid (CSF) barrier. CSF and interstitial fluid turnover, and FcRn mediated transport of mAbs are accounted for. The model was first used to characterize published and in-house pharmacokinetic (PK) data on the disposition of mAbs in rat brain, including the data on PK of mAb in different regions of brain determined using microdialysis. Majority of model parameters were fixed based on literature reported values, and only 3 parameters were estimated using rat data. The rat PBPK model was translated to mouse, monkey, and human, simply by changing the values of physiological parameters corresponding to each species. The translated PBPK models were validated by a priori predicting brain PK of mAbs in all three species, and comparing predicted exposures with observed data. The platform PBPK model was able to a priori predict all the validation PK profiles reasonably well (within threefold), without estimating any parameters. As such, the platform PBPK model presented here provides an unprecedented quantitative tool for prediction of mAb PK at the site-of-action in the brain, and preclinical-to-clinical translation of mAbs being developed against central nervous system (CNS) disorders. The proposed model can be further expanded to account for target engagement, disease pathophysiology, and novel mechanisms, to support discovery and development of novel CNS targeting mAbs.

Orphan drug development: the increasing role of clinical pharmacology Abstract Over the last few decades there has been a paradigm shift in orphan drug research and development. The development of the regulatory framework, establishment of rare disease global networks that support drug developments, and advances in technology, has resulted in tremendous growth in orphan drug development. Nevertheless, several challenges during orphan drug development such as economic constraints; insufficient clinical information; fewer patients and thus inadequate power; etc. still exist. While the standard regulatory requirements for drug approval stays the same, applications of scientific judgment and regulatory flexibility is significantly important to help meeting some of the immense unmet medical need in rare diseases. Clinical pharmacology presents a vital role in accelerating orphan drug development and overcoming some of these challenges. This review highlights the critical contributions of clinical pharmacology in orphan drug development; for example, dose finding, optimizing clinical trial design, indication expansion, and population extrapolation. Examples of such applications are reviewed in this article.

Correction to: Routine clinical care data for population pharmacokinetic modeling: the case for Fanhdi/Alphanate in hemophilia A patients The article Routine clinical care data for population pharmacokinetic modeling: the case for Fanhdi/Alphanate in hemophilia A patients, written by Pierre Chelle, Cindy H. T. Yeung, Santiago Bonanad, Juan Cristóbal Morales Muñoz, Margareth C. Ozelo, Juan Eduardo Megías Vericat, Alfonso Iorio, Jeffrey Spears, Roser Mir, Andrea Edginton, was originally published electronically on the publisher's internet portal (currently SpringerLink) on 21 May 2019 without open access.

FDA’s Office of Orphan Products Development: providing incentives to promote the development of products for rare diseases Abstract There are nearly 30 million Americans that suffer from at least one of the more than 7000 rare diseases identified to date. Therapies for treating, preventing, or diagnosing rare diseases have been limited due to various reasons. Incentives are provided to sponsors in an effort to promote the development of therapies for rare diseases and to encourage the availability of therapeutically superior drugs or biologics. This paper will discuss the mission of the Office of Orphan Products Development within the Food and Drug Administration (FDA), the specific programs within the office and the relation to incentives provided, achievements of the programs, and continued challenges in rare disease product development.

Development of visual predictive checks accounting for multimodal parameter distributions in mixture models Abstract The assumption of interindividual variability being unimodally distributed in nonlinear mixed effects models does not hold when the population under study displays multimodal parameter distributions. Mixture models allow the identification of parameters characteristic to a subpopulation by describing these multimodalities. Visual predictive check (VPC) is a standard simulation based diagnostic tool, but not yet adapted to account for multimodal parameter distributions. Mixture model analysis provides the probability for an individual to belong to a subpopulation (IPmix) and the most likely subpopulation for an individual to belong to (MIXEST). Using simulated data examples, two implementation strategies were followed to split the data into subpopulations for the development of mixture model specific VPCs. The first strategy splits the observed and simulated data according to the MIXEST assignment. A shortcoming of the MIXEST-based allocation strategy was a biased allocation towards the dominating subpopulation. This shortcoming was avoided by splitting observed and simulated data according to the IPmix assignment. For illustration purpose, the approaches were also applied to an irinotecan mixture model demonstrating 36% lower clearance of irinotecan metabolite (SN-38) in individuals with UGT1A1 homo/heterozygote versus wild-type genotype. VPCs with segregated subpopulations were helpful in identifying model misspecifications which were not evident with standard VPCs. The new tool provides an enhanced power of evaluation of mixture models.

Operating characteristics of stepwise covariate selection in pharmacometric modeling Abstract Stepwise covariate modeling (SCM) is a widely used tool in pharmacometric analyses to identify covariates that explain between-subject variability (BSV) in exposure and exposure–response relationships. However, this approach has several potential weaknesses, including over-estimated covariate effect and incorrect selection of covariates due to collinearity. In this work, we investigated the operating characteristics (i.e., accuracy, precision, and power) of SCM in a controlled setting by simulating sixteen scenarios with up to four covariate relationships. The SCM analysis showed a decrease in the power to detect the true covariates as model complexity increased. Furthermore, false highly correlated covariates were frequently selected in place of or in addition to the true covariates. Relative root mean square errors (RMRSE) ranged from 1 to 51% for the fixed effects parameters, increased with the number of covariates included in the model, and were slightly higher than the RMRSE obtained with a simple re-estimation exercise with the true model (i.e., stochastic simulation and estimation). RMRSE for BSV increased with the number of covariates included in the model, with a covariance parameter RMRSE of almost 135% in the most complex scenario. Loose boundary conditions on the continuous covariate power relation appeared to have an impact on the covariate model selection in SCM. A stricter boundary condition helped achieve high power (> 90%), even in the most complex scenario. Finally, reducing the sample size in terms of number of subjects or number of samples proved to have an impact on the power to detect the correct model.

Guiding dose selection of monoclonal antibodies using a new parameter (AFTIR) for characterizing ligand binding systems Abstract Guiding the dose selection for monoclonal antibody oncology drugs is often done using methods for predicting the receptor occupancy of the drug in the tumor. In this manuscript, previous work on characterizing target inhibition at steady state using the AFIR metric (Stein and Ramakrishna in CPT Pharmacomet Syst Pharmacol 6(4):258–266, 2017) is extended to include a “target-tissue” compartment and the shedding of membrane-bound targets. A new potency metric average free tissue target to initial target ratio (AFTIR) at steady state is derived, and it depends on only four key quantities: the equilibrium binding constant, the fold-change in target expression at steady state after binding to drug, the biodistribution of target from circulation to target tissue, and the average drug concentration in circulation. The AFTIR metric is useful for guiding dose selection, for efficiently performing sensitivity analyses, and for building intuition for more complex target mediated drug disposition models. In particular, reducing the complex, physiological model to four key parameters needed to predict target inhibition helps to highlight specific parameters that are the most important to estimate in future experiments to guide drug development.