Practical QSP application from the preclinical phase to enhance the probability of clinical success: Insights from case studies in oncology.

Quantitative Systems Pharmacology (QSP) has emerged as a promising modeling and simulation (M&S) approach in drug development, with potential to improve clinical success rates. While conventional M&S has significantly contributed to quantitative understanding in late preclinical and clinical phases, it falls short in explaining unexpected phenomena and testing hypotheses in the early research phase. QSP presents a solution to these limitations. To harness the full potential of QSP in early preclinical stages, preclinical modelers who are familiar with conventional M&S need to update their understanding of the differences between conventional M&S and QSP. This review focuses on QSP applications during the preclinical stage, citing case examples and sharing our experiences in oncology. We emphasize the critical role of QSP in increasing the probability of success for clinical proof of concept (PoC) when applied from the early preclinical stage. Enhancing the quality of both hypotheses and QSP models from early preclinical stage is of critical importance. Once a QSP model achieves credibility, it facilitates predictions of clinical responses and potential biomarkers. We propose that sequential QSP applications from preclinical stages can improve success rates of clinical PoC, and emphasize the importance of refining both hypotheses and QSP models throughout the process.

Application of hybrid approach based on empirical and physiological concept for predicting pharmacokinetics in humans--usefulness of exponent on prospective evaluation of predictability.

We developed a hybrid method for predicting plasma concentration-time curves in humans by integrating species differences in in vitro intrinsic clearance (CL(int)) into the Dedrick approach based on the allometry concept. With prediction of clearance (CL) by allometric scaling, taking in vitro CL(int) into consideration improved the accuracy and reduced the average fold error from 2.72 to 1.99. With the hybrid approach of applying the same concept to the Dedrick approach, the predictability of plasma concentration profiles was compared with the results of the conventional Dedrick approach and the physiologically based pharmacokinetic model using 15 compounds with widely ranging physicochemical and pharmacokinetic profiles. The hybrid approach showed the highest predictability among the examined methods. For CL and the apparent volume of distribution at the steady state (V(ss)), the relationship between the exponent of allometric equation and fold error was also evaluated with the hybrid approach. The relationship appeared to be a horseshoe curve. Six compounds with exponents ranging from 0.7 to 1.1 for both CL and V(ss) [antipyrine, caffeine, epiroprim, propafenone, theophylline, and verapamil] displayed higher predictability. Three compounds with an exponent ranging from 0.7 to 1.1 for CL showed better predictability for CL, and the other four compounds appeared to display similar relationship between the exponent and predictability for V(ss). These findings indicated that the exponent becomes a preliminary index to speculate on predictability. Combination of the hybrid approach and exponent allows us to prospectively draw human plasma concentration-time curves, with the implication of possible prediction accuracy prior to clinical studies.

Virtual clinical trial simulations for a novel KRASG12C inhibitor (ASP2453) in non-small cell lung cancer.

KRAS is a small GTPase family protein that relays extracellular growth signals to cell nucleus. KRASG12C mutations lead to constitutive proliferation signaling and are prevalent across human cancers. ASP2453 is a novel, highly potent, and selective inhibitor of KRASG12C . Although preclinical data suggested impressive efficacy, it remains unclear whether ASP2453 will show more favorable clinical response compared to more advanced competitors, such as AMG 510. Here, we developed a quantitative systems pharmacology (QSP) model linking KRAS signaling to tumor growth in patients with non-small cell lung cancer. The model was parameterized using in vitro ERK1/2 phosphorylation and in vivo xenograft data for ASP2453. Publicly disclosed clinical data for AMG 510 were used to generate a virtual population, and tumor size changes in response to ASP2453 and AMG 510 were simulated. The QSP model predicted ASP2453 exhibits greater clinical response than AMG 510, supporting potential differentiation and critical thinking for clinical trials.

Quantitative Systems Pharmacology Approaches for Immuno-Oncology: Adding Virtual Patients to the Development Paradigm.

Drug development in oncology commonly exploits the tools of molecular biology to gain therapeutic benefit through reprograming of cellular responses. In immuno-oncology (IO) the aim is to direct the patient's own immune system to fight cancer. After remarkable successes of antibodies targeting PD1/PD-L1 and CTLA4 receptors in targeted patient populations, the focus of further development has shifted toward combination therapies. However, the current drug-development approach of exploiting a vast number of possible combination targets and dosing regimens has proven to be challenging and is arguably inefficient. In particular, the unprecedented number of clinical trials testing different combinations may no longer be sustainable by the population of available patients. Further development in IO requires a step change in selection and validation of candidate therapies to decrease development attrition rate and limit the number of clinical trials. Quantitative systems pharmacology (QSP) proposes to tackle this challenge through mechanistic modeling and simulation. Compounds' pharmacokinetics, target binding, and mechanisms of action as well as existing knowledge on the underlying tumor and immune system biology are described by quantitative, dynamic models aiming to predict clinical results for novel combinations. Here, we review the current QSP approaches, the legacy of mathematical models available to quantitative clinical pharmacologists describing interaction between tumor and immune system, and the recent development of IO QSP platform models. We argue that QSP and virtual patients can be integrated as a new tool in existing IO drug development approaches to increase the efficiency and effectiveness of the search for novel combination therapies.

[An introduction to QSP modeling for pharmacologists].

Quantitative systems pharmacology (QSP) is an emerging field of modeling technologies that describes the dynamic interaction between biological systems and drugs. Recently, QSP is increasingly being applied to pharmaceutical drug discovery and development, and used for various types of decision makings. In contrast to empirical and statistical models, QSP represents complex systems of human physiology by integrating comprehensive biological information, hence, it can address various purposes including target and/or disease-related biomarker identification, hypothesis testing, and prediction of clinical efficacy or toxicity. On the other hand, structures of QSP models become quite complicated with huge amount of biological components, therefore, close collaboration between pharmacologists having profound knowledge of biology and drug metabolism and pharmacokinetics (DMPK) scientists, experts of model building, is crucial for QSP development and implementation. This article introduces, from DMPK scientists to pharmacologists, main features of QSP and its applications in pharmaceutical industries, and discusses challenges and future perspectives for effective utilization in drug discovery and development.

Effects of Adjuvant-Induced Inflammation on Disposition of Diclofenac and Its Metabolites in Perfused Rat Liver.

The reactive metabolites of diclofenac (DF) such as 1-O-acyl glucuronide (DF-Glu) are hypothesized to result in idiosyncratic hepatotoxicity. However, it is unclear whether inflammation affects the hepatic disposition of DF and its metabolites. To clarify the alterations in the disposition of DF and its metabolites in inflammatory conditions, we performed in situ perfused rat liver experiments. Using adjuvant arthritis rats as a model of inflammation, the elimination of DF, 4'-hydroxydiclofenac, and DF-Glu from the perfusate was observed to be delayed in comparison with the control. Parameter sensitivity analysis for hepatic DF disposition revealed that the area under the plasma concentration-time curve (AUC) and the maximum concentration (Cmax) of DF-Glu in the liver markedly increased along with a decrease in intrinsic excretion clearance of DF-Glu (CLint,bile,Glu) and an increase in intrinsic glucuronidation clearance (CLint,Glu) of DF-Glu. It is possible that the elimination of DF-Glu from the perfusate in adjuvant arthritis rats was delayed via reduction of biliary excretion of DF-Glu.

Application of a physiologically based pharmacokinetic model informed by a top-down approach for the prediction of pharmacokinetics in chronic kidney disease patients.

Quantitative prediction of the impact of chronic kidney disease (CKD) on drug disposition has become important for the optimal design of clinical studies in patients. In this study, clinical data of 151 compounds under CKD conditions were extensively surveyed, and alterations in pharmacokinetic parameters were evaluated. In CKD patients, the unbound hepatic intrinsic clearance decreased to a similar extent for drugs eliminated via hepatic metabolism by cytochrome P450, UDP-glucuronosyltransferase, and other mechanisms. Renal clearance showed a similar decrease to glomerular filtration rate, irrespective of the contribution of tubular secretion. The scaling factor (SF) obtained from the interquartile range of the relative change in each parameter was applied to the well-stirred model to predict clearance in patients. Hepatic and renal clearance could be successfully predicted for approximately half and two-thirds, respectively, of the applied compounds, showing the high utility of SFs. SFs were also introduced to a physiologically based pharmacokinetic (PBPK) model, and the plasma concentration profiles of 12 model compounds with different elimination pathways were predicted for CKD patients. The PBPK model combined with SFs provided good predictability for plasma concentration. The developed PBPK model with information on SFs would accelerate translational research in drug development by predicting pharmacokinetics in CKD patients.

Development of a hybrid physiologically based pharmacokinetic model with drug-specific scaling factors in rat to improve prediction of human pharmacokinetics.

Accurate prediction of pharmacokinetics (PK) in humans has been a vital part of drug discovery. The aims of this study are to verify the usefulness of scaling factors for clearance (CL) and apparent volume of distribution at the steady state (Vss ) estimated from the difference between observed and predicted PK profiles in rats for human PK prediction, and to develop a novel hybrid physiologically based pharmacokinetic (PBPK) model with the two scaling factors. The human prediction accuracies for CL with in vitro-in vivo extrapolation and Vss with a tissue composition model were improved by using rat-scaling factors. This improvement was explainable by data that the scaling factors for CL and Vss in rats were correlated with those in humans. The predictability of plasma concentration-time profiles by the hybrid PBPK model incorporating two scaling factors was compared mainly with that by the conventional PBPK model. The hybrid PBPK model yielded higher prediction accuracy for plasma concentrations than the conventional method. Furthermore, we proposed a tiered approach using the three prediction methods, including the hybrid Dedrick approach, that were previously reported (Sayama H, Komura H, Kogayu M. 2013. Drug Metab Dispos 41:498-507), taking the available information in the individual stages of drug discovery and development into consideration.