Estimation of feasible solution space using Cluster Newton Method: application to pharmacokinetic analysis of irinotecan with physiologically-based pharmacokinetic models

Parameter grouping and co-estimation in physiologically based kinetic models using genetic algorithms

Physiologically based kinetic (PBK) models are widely used in pharmacology and toxicology for predicting the internal disposition of substances upon exposure, voluntarily or not. Due to their complexity, a large number of model parameters need to be estimated, either through in silico tools, in vitro experiments, or by fitting the model to in vivo data. In the latter case, fitting complex structural models on in vivo data can result in overparameterization and produce unrealistic parameter estimates. To address these issues, we propose a novel parameter grouping approach, which reduces the parametric space by co-estimating groups of parameters across compartments. Grouping of parameters is performed using genetic algorithms and is fully automated, based on a novel goodness-of-fit metric. To illustrate the practical application of the proposed methodology, two case studies were conducted. The first case study demonstrates the development of a new PBK model, while the second focuses on model refinement. In the first case study, a PBK model was developed to elucidate the biodistribution of titanium dioxide (TiO2) nanoparticles in rats following intravenous injection. A variety of parameter estimation schemes were employed. Comparative analysis based on goodness-of-fit metrics demonstrated that the proposed methodology yields models that outperform standard estimation approaches, while utilizing a reduced number of parameters. In the second case study, an existing PBK model for perfluorooctanoic acid (PFOA) in rats was extended to incorporate additional tissues, providing a more comprehensive portrayal of PFOA biodistribution. Both models were validated through independent in vivo studies to ensure their reliability.

Applications of model simulation in pharmacological fields and the problems of theoretical reliability

The use of mathematical models has become increasingly prevalent in pharmacological fields, particularly in drug development processes. These models are instrumental in tasks such as designing clinical trials and assessing factors like efficacy, toxicity, and clinical practice. Various types of models have been developed and documented. Nevertheless, emphasizing the reliability of parameter values is crucial, as they play a pivotal role in shaping the behavior of the system. In some instances, parameter values reported previously are treated as fixed values, which can lead to convergence towards values that deviate substantially from those found in actual biological systems. This is especially true when parameter values are determined through fitting to limited observations. To mitigate this risk, the reuse of parameter values from previous reports should be approached with a critical evaluation of their validity. Currently, there is a proposal for a simultaneous search for plausible values for all parameters using comprehensive search algorithms in both pharmacokinetic and pharmacodynamic or systems pharmacological models. Implementing these methodologies can help address issues related to parameter determination. Furthermore, integrating these approaches with methods developed in the field of machine-learning field has the potential to enhance the reliability of parameter values and the resulting model outputs.

Development of a physiologically based pharmacokinetic model to predict irinotecan disposition during inflammation

Irinotecan, a first-line chemotherapy for gastrointestinal (GI) cancers has been causing fatal toxicities like bloody diarrhea and steatohepatitis for years. Irinotecan goes through multiple-step drug metabolism after injection and one of its intermediates 7-ethyl-10-hydroxy-camptothecin (SN-38) is responsible for irinotecan side effect. However, it is unclear what is the disposition kinetics of SN-38 in the organs subjected to toxicity. No studies ever quantified the effect of each enzyme or transporter on SN-38 distribution. In current study, we established a new physiologically based pharmacokinetic (PBPK) model to predict the disposition kinetics of irinotecan. The PBPK model was calibrated with in-house mouse pharmacokinetic data and evaluated with external datasets from the literature. We separated the contribution of each parameters in irinotecan pharmacokinetics by calculating the normalized sensitivity coefficient (NSC). The model gave robust prediction of SN-38 distribution in GI tract, the site of injury. We identified that bile excretion and UDP-glucuronosyltransferases (UGT) played more important roles than fecal excretion and renal clearance in SN-38 pharmacokinetics. Our NSC showed that the impact of enzyme and transporter on irinotecan and SN-38 pharmacokinetics evolved when time continued. Additionally, we mapped out the effect of inflammation on irinotecan metabolic pathways with PBPK modelling. We discovered that inflammation significantly increased the blood and liver exposure of irinotecan and SN-38 in the mice receiving bacterial endotoxin. Inflammation suppressed UGT, microbial metabolism but increased fecal excretion. The present PBPK model can serve as an efficacious and versatile tool to quantitively assess the risk of irinotecan toxicity.

Utilization of Physiologically Based Pharmacokinetic Modeling in Clinical Pharmacology and Therapeutics: an Overview

The purpose of this review was to assess the advancement of applications for physiologically based pharmacokinetic (PBPK) modeling in various therapeutic areas. We conducted a PubMed search, and 166 articles published between 2012 and 2018 on FDA-approved drug products were selected for further review. Qualifying publications were summarized according to therapeutic area, medication(s) studied, pharmacokinetic model type utilized, simulator program used, and the applications of that modeling. The results showed a 13-fold increase in the number of papers published from 2012 to 2018, with the largest proportion of articles dedicated to the areas of infectious diseases, oncology, and neurology, and application extensions including prediction of drug-drug interactions due to metabolism and/or transporter-mediated effects and understanding drug kinetics in special populations. In addition, we profiled several high-impact studies whose results were used to guide package insert information and formulate dose recommendations. These results show that while utilization of PBPK modeling has drastically increased over the past several years, regulatory support, lack of easy-to-use systems for clinicians, and challenges with model validation remain major challenges for the widespread adoption of this practice in institutional and ambulatory settings. However, PBPK modeling will continue to be a useful tool in the future to assess therapeutic drug monitoring and the growing field of personalized medicine.

A Fast Parameter Identification Framework for Personalized Pharmacokinetics

This paper introduces a novel framework for fast parameter identification of personalized pharmacokinetic problems. Given one sample observation of a new subject, the framework predicts the parameters of the subject based on prior knowledge from a pharmacokinetic database. The feasibility of this framework was demonstrated by developing a new algorithm based on the Cluster Newton method, namely the constrained Cluster Newton method, where the initial points of the parameters are constrained by the database. The algorithm was tested with the compartmental model of propofol on a database of 59 subjects. The average overall absolute percentage error based on constrained Cluster Newton method is 12.10% with the threshold approach, and 13.42% with the nearest-neighbor approach. The average computation time of one estimation is 13.10 seconds. Using parallel computing, the average computation time is reduced to 1.54 seconds, achieved with 12 parallel workers. The results suggest that the proposed framework can effectively improve the prediction accuracy of the pharmacokinetic parameters with limited observations in comparison to the conventional methods. Computation cost analyses indicate that the proposed framework can take advantage of parallel computing and provide solutions within practical response times, leading to fast and accurate parameter identification of pharmacokinetic problems.

Accurate Estimation of In Vivo Inhibition Constants of Inhibitors and Fraction Metabolized of Substrates with Physiologically Based Pharmacokinetic Drug-Drug Interaction Models Incorporating Parent Drugs and Metabolites of Substrates with Cluster Newton Method

The accurate estimation of "in vivo" inhibition constants (K i) of inhibitors and fraction metabolized (f m) of substrates is highly important for drug-drug interaction (DDI) prediction based on physiologically based pharmacokinetic (PBPK) models. We hypothesized that analysis of the pharmacokinetic alterations of substrate metabolites in addition to the parent drug would enable accurate estimation of in vivo K i and f m Twenty-four pharmacokinetic DDIs caused by P450 inhibition were analyzed with PBPK models using an emerging parameter estimation method, the cluster Newton method, which enables efficient estimation of a large number of parameters to describe the pharmacokinetics of parent and metabolized drugs. For each DDI, two analyses were conducted (with or without substrate metabolite data), and the parameter estimates were compared with each other. In 17 out of 24 cases, inclusion of substrate metabolite information in PBPK analysis improved the reliability of both K i and f m Importantly, the estimated K i for the same inhibitor from different DDI studies was generally consistent, suggesting that the estimated K i from one study can be reliably used for the prediction of untested DDI cases with different victim drugs. Furthermore, a large discrepancy was observed between the reported in vitro K i and the in vitro estimates for some inhibitors, and the current in vivo K i estimates might be used as reference values when optimizing in vitro-in vivo extrapolation strategies. These results demonstrated that better use of substrate metabolite information in PBPK analysis of clinical DDI data can improve reliability of top-down parameter estimation and prediction of untested DDIs.

Quantitative Analysis of Complex Drug-Drug Interactions between Cerivastatin and Metabolism/Transport Inhibitors Using Physiologically Based Pharmacokinetic Modeling

Cerivastatin (CER) was withdrawn from the world market because of lethal rhabdomyolysis. Coadministrations of CER and cyclosporine A (CsA) or gemfibrozil (GEM) have been reported to increase the CER blood concentration. CsA is an inhibitor of organic anion transporting polypeptide (OATP)1B1 and CYP3A4, and GEM and its glucuronide (GEM-glu) inhibit OATP1B1 and CYP2C8. The purpose of this study was to describe the transporter-/enzyme-mediated drug-drug interactions (DDIs) of CER with CsA or GEM based on unified physiologically based pharmacokinetic (PBPK) models and to investigate whether the DDIs can be quantitatively analyzed by a bottom-up approach. Initially, the PBPK models for CER and GEM/GEM-glu were constructed based on the previously reported standard protocols. Next, the drug-dependent parameters were optimized by Cluster Newton Method. Thus, described concentration-time profiles for CER and GEM/GEM-glu agreed well with the clinically observed data. The DDIs were then simulated using the established PBPK models with previously obtained in vitro inhibition constants of CsA or GEM/GEM-glu against the OATP1B1 and cytochrome P450s. DDIs with the inhibitors were underestimated compared with observed data using the geometric means of reported values. To search for better described parameters within the range of in vitro values, sensitivity analyses were performed for DDIs of CER. Using the in vitro parameter sets selected by sensitivity analyses, these DDIs were well reproduced, indicating that the present PBPK models were able to describe adequately the clinical DDIs based on a bottom-up approach. The approaches in this study would be applicable to the prediction of other DDIs involving both transporters and metabolic enzymes.

Application of PBPK Modeling and Virtual Clinical Study Approaches to Predict the Outcomes of CYP2D6 Genotype-Guided Dosing of Tamoxifen

The Tamoxifen Response by CYP2D6 Genotype‐based Treatment‐1 (TARGET‐1) study (n = 180) was conducted from 2012–2017 in Japan to determine the efficacy of tamoxifen dosing guided by cytochrome P450 2D6 (CYP2D6) genotypes. To predict its outcomes prior to completion, we constructed the comprehensive physiologically based pharmacokinetic (PBPK) models of tamoxifen and its metabolites and performed virtual TARGET‐1 studies. Our analyses indicated that the expected probability to achieve the end point (demonstrating the superior efficacy of the escalated tamoxifen dose over the standard dose in patients carrying CYP2D6 variants) was 0.469 on average. As the population size of this virtual clinical study (VCS) increased, the expected probability was substantially increased (0.674 for n = 260). Our analyses also informed that the probability to achieve the end point in the TARGET‐1 study was negatively impacted by a large variability in endoxifen levels. Our current efforts demonstrate the promising utility of the PBPK modeling and VCS approaches in prospectively designing effective clinical trials.

Constraint-based perturbation analysis with cluster Newton method: a case study of personalized parameter estimations with irinotecan whole-body physiologically based pharmacokinetic model

Background

Drug development considering individual varieties among patients becomes crucial to improve clinical development success rates and save healthcare costs. As a useful tool to predict individual phenomena and correlations among drug characteristics and individual varieties, recently, whole-body physiologically based pharmacokinetic (WB- PBPK) models are getting more attention. WB-PBPK models generally have a lot of drug-related parameters that need to be estimated, and the estimations are difficult because the observed data are limited. Furthermore, parameter estimation in WB-PBPK models may cause overfitting when applying to individual clinical data such as urine/feces drug excretion for each patient in which Cluster Newton Method (CNM) is applicable for parameter estimation. In order to solve this issue, we came up with the idea of constraint-based perturbation analysis of the CNM. The effectiveness of our approach is demonstrated in the case of irinotecan WB-PBPK model using common organ-specific tissue-plasma partition coefficients (Kp) among the patients as constraints in WB-PBPK parameter estimation.

Results

We find strong correlations between age, renal clearance and liver functions in irinotecan WB-PBPK model with personalized physiological parameters by observing the distributions of optimized values of strong convergence drug-related parameters using constraint-based perturbation analysis on CNM. The constraint-based perturbation analysis consists of the following three steps: (1) Estimation of all drug-related parameters for each patient; the parameters include organ-specific Kp. (2) Fixing suitable values of Kp for each organ among all patients identically. (3) Re-estimation of all drug-related parameters other than Kp by using the fixed values of Kp as constraints of CNM.

Conclusions

Constraint-based perturbation analysis could yield new findings when using CNM with appropriate constraints. This method is a new technique to find suitable values and important insights that are masked by CNM without constraints.

Electronic supplementary material

The online version of this article (10.1186/s12918-017-0513-2) contains supplementary material, which is available to authorized users.

Quantitative systems pharmacology and the personalized drug-microbiota-diet axis

Precision medicine is an emerging paradigm that aims at maximizing the benefits and minimizing the adverse effects of drugs. Realistic mechanistic models are needed to understand and limit heterogeneity in drug responses. While pharmacokinetic models describe in detail a drug's absorption and metabolism, they generally do not account for individual variations in response to environmental influences, in addition to genetic variation. For instance, the human gut microbiota metabolizes drugs and is modulated by diet, and it exhibits significant variation among individuals. However, the influence of the gut microbiota on drug failure or drug side effects is under-researched. Here, we review recent advances in computational modeling approaches that could contribute to a better, mechanism-based understanding of drug-microbiota-diet interactions and their contribution to individual drug responses. By integrating systems biology and quantitative systems pharmacology with microbiology and nutrition, the conceptually and technologically demand for novel approaches could be met to enable the study of individual variability, thereby providing breakthrough support for progress in precision medicine.

Analysis of Nonlinear Pharmacokinetics of a Highly Albumin-Bound Compound: Contribution of Albumin-Mediated Hepatic Uptake Mechanism

The cause of nonlinear pharmacokinetics (PK) (more than dose-proportional increase in exposure) of a urea derivative under development (compound A: anionic compound [pKa: 4.4]; LogP: 6.5; and plasma protein binding: 99.95%) observed in a clinical trial was investigated. Compound A was metabolized by CYP3A4, UGT1A1, and UGT1A3 with unbound K_m of 3.3-17.8 μmol/L. OATP1B3-mediated uptake of compound A determined in the presence of human serum albumin (HSA) showed that unbound K_m and V_max decreased with increased HSA concentration. A greater decrease in unbound K_m than in V_max resulted in increased uptake clearance (V_max/unbound K_m) with increased HSA concentration, the so-called albumin-mediated uptake. At 2% HSA concentration, unbound K_m was 0.00657 μmol/L. A physiologically based PK model assuming saturable hepatic uptake nearly replicated clinical PK of compound A. Unbound K_m for hepatic uptake estimated from the model was 0.000767 μmol/L, lower than the in vitro unbound K_m at 2% HSA concentration, whereas decreased K_m with increased concentration of HSA in vitro indicated lower K_m at physiological HSA concentration (4%-5%). In addition, unbound K_m values for metabolizing enzymes were much higher than unbound K_m for OATP1B3, indicating that the nonlinear PK of compound A is primarily attributed to saturated OATP1B3-mediated hepatic uptake of compound A.

Virtual Clinical Studies to Examine the Probability Distribution of the AUC at Target Tissues Using Physiologically-Based Pharmacokinetic Modeling: Application to Analyses of the Effect of Genetic Polymorphism of Enzymes and Transporters on Irinotecan Induced Side Effects

Purpose

To establish a physiologically-based pharmacokinetic (PBPK) model for analyzing the factors associated with side effects of irinotecan by using a computer-based virtual clinical study (VCS) because many controversial associations between various genetic polymorphisms and side effects of irinotecan have been reported.

Methods

To optimize biochemical parameters of irinotecan and its metabolites in the PBPK modeling, a Cluster Newton method was introduced. In the VCS, virtual patients were generated considering the inter-individual variability and genetic polymorphisms of enzymes and transporters.

Results

Approximately 30 sets of parameters of the PBPK model gave good reproduction of the pharmacokinetics of irinotecan and its metabolites. Of these, 19 sets gave relatively good description of the effect of UGT1A1 *28 and SLCO1B1 c.521T>C polymorphism on the SN-38 plasma concentration, neutropenia, and diarrhea observed in clinical studies reported mainly by Teft et al. (Br J Cancer. 112(5):857-65, 20). VCS also indicated that the frequency of significant association of biliary index with diarrhea was higher than that of UGT1A1 *28 polymorphism.

Conclusion

The VCS confirmed the importance of genetic polymorphisms of UGT1A1 *28 and SLCO1B1 c.521T>C in the irinotecan induced side effects. The VCS also indicated that biliary index is a better biomarker of diarrhea than UGT1A1 *28 polymorphism.

Electronic supplementary material

The online version of this article (doi:10.1007/s11095-017-2153-z) contains supplementary material, which is available to authorized users.

HD Physiology Project-Japanese efforts to promote multilevel integrative systems biology and physiome research

The HD Physiology Project is a Japanese research consortium that aimed to develop methods and a computational platform in which physiological and pathological information can be described in high-level definitions across multiple scales of time and size. During the 5 years of this project, an appropriate software platform for multilevel functional simulation was developed and a whole-heart model including pharmacokinetics for the assessment of the proarrhythmic risk of drugs was developed. In this article, we outline the description and scientific strategy of this project and present the achievements and influence on multilevel integrative systems biology and physiome research.

Robust model predictive control for optimal continuous drug administration

In this paper the model predictive control (MPC) technology is used for tackling the optimal drug administration problem. The important advantage of MPC compared to other control technologies is that it explicitly takes into account the constraints of the system. In particular, for drug treatments of living organisms, MPC can guarantee satisfaction of the minimum toxic concentration (MTC) constraints. A whole-body physiologically-based pharmacokinetic (PBPK) model serves as the dynamic prediction model of the system after it is formulated as a discrete-time state-space model. Only plasma measurements are assumed to be measured on-line. The rest of the states (drug concentrations in other organs and tissues) are estimated in real time by designing an artificial observer. The complete system (observer and MPC controller) is able to drive the drug concentration to the desired levels at the organs of interest, while satisfying the imposed constraints, even in the presence of modelling errors, disturbances and noise. A case study on a PBPK model with 7 compartments, constraints on 5 tissues and a variable drug concentration set-point illustrates the efficiency of the methodology in drug dosing control applications. The proposed methodology is also tested in an uncertain setting and proves successful in presence of modelling errors and inaccurate measurements.

InCoB2013 introduces Systems Biology as a major conference theme

The Asia-Pacific Bioinformatics Network (APBioNet) held the first International Conference on Bioinformatics (InCoB) in Bangkok in 2002 to promote North-South networking. Commencing as a forum for Asia-Pacific researchers to interact with and learn from with scientists of developed countries, InCoB has become a major regional bioinformatics conference, with participants from the region as well as North America and Europe. Since 2006, InCoB has selected the best submissions for publication in BMC Bioinformatics. In response to the growth and maturation of data-driven approaches, InCoB added BMC Genomics in 2009 and with the introduction of this conference supplement, BMC Systems Biology to its journal choices for submitting authors. Co-hosting InCoB2013 with the second International Conference for Translational Bioinformatics (ICTBI) is in line with InCoB's support for the current trend in taking bioinformatics to the bedside, along with a systems approach to solving biological problems.