Quantitative Systems Pharmacology Model Research Articles

Abstract 2796: Predicting in vivo efficacy from in vitro data: Quantitative systems pharmacology modeling for an epigenetic modifier drug in cancer

Abstract Epigenetic modulators are increasing in prominence as potential cancer therapies. These drugs achieve their effectiveness by inducing transcriptional changes that can inhibit cancer progression. Here, we focus on a potent and selective covalent small molecule inhibitor of LSD1 (RO7051750/ORY-1001), a lysine-specific histone demethylase enzyme. LSD1 inhibition causes epigenetic reprogramming of cancer cells by downregulating pro-proliferative neuroendocrine genes, inducing a pro-differentiation, cytostatic effect. Furthermore, the durability of this response is enhanced by covalent drug binding and the resulting dependence on target turnover. To explore the impact of dose and scheduling on the pharmacodynamic profiles underlying treatment efficacy, we developed a predictive model of drug effect, which incorporates experimentally-derived pharmacokinetic and pharmacodynamic data. This mechanistic mathematical model was calibrated based on in vitro cell-type specific kinetic data, which possesses high dimensionality across time points and dose ranges. The model describes drug-induced target engagement of LSD1, which acts as a transcriptional regulator of neuroendocrine gene, GRP. GRP levels drive reversible epigenetic “switching” between a proliferating (denoted P) and a quiescent (denoted Q) cell population (low GRP: P to Q; high GRP: Q to P), capturing the cytostatic effects of the drug. The model was trained with in vitro measurements of target engagement, GRP mRNA levels, and cell growth inhibition (including drug-free cell growth) across various time points and dosages from a small cell lung cancer cell line (NCI-H510A). Importantly, GRP mRNA and cell viability dynamics were also captured upon drug withdrawal, which enables the modeling of drug effect durability. The resulting model quantitatively describes the relationship between drug dose, target engagement, biomarker and cell growth dynamics in vitro. To scale to the in vivo setting, we integrated the in vitro-trained pharmacodynamic model with a pharmacokinetic model describing drug distribution and clearance in mouse. This hybrid model accurately predicted in vivo tumor growth dynamics in the NCI-H510A mouse xenograft across a range of doses and schedules. We used the model to explore the target engagement and biomarker profiles underlying treatment efficacy, both in vitro and in vivo. In conclusion, by training our model on key cell-type specific in vitro kinetic data we were able to accurately predict anti-tumor growth effects in vivo. This quantitative modeling framework highlights the biological profiles underlying efficacy for this latest generation of target-selective epigenetic modifying drugs. Citation Format: Mehdi Bouhaddou, Li J. Yu, Serena Lunardi, Spyros K. Stamatelos, Fiona Mack, James M. Gallo, Marc R. Birtwistle, Antje-Christine Walz. Predicting in vivo efficacy from in vitro data: Quantitative systems pharmacology modeling for an epigenetic modifier drug in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2796.

Abstract 4266: Computational exploration of mechanistic determinants of ADC pharmacokinetics using QSP modeling strategies

Abstract The pharmacokinetics of antibody drug conjugate (ADC) therapeutics typically show a discrepancy between the PK of total antibody (conjugated and unconjugated antibody) and that of conjugated antibody, carrying one or more payload molecules. This discrepancy is often attributed strictly to deconjugation (Kamath, 2014), however recent evidence suggests that the underlying mechanisms may be more complex. This work employs a computational quantitative systems pharmacology (QSP) approach that generates a hypotheses to better understand the impact of drug antibody ratio (DAR) and the resulting changes in molecular properties on overall PK and relative payload disposition as observed in preclinical and clinical studies. Our work establishes the benefit of using computational models to design novel ADCs and to optimize the discovery and development of existing ADCs to better enable clinical trial design.

TRANSLATIONAL QUANTITATIVE SYSTEMS PHARMACOLOGY MODEL OF TAUOPATHIES

Quantitative systems pharmacology modelling can be valuable tool for understanding such a multifaceted process as tau protein accumulation. For prediction of the results of tau targeted therapy we propose a mechanistic model describing such tauopathies as Alzheimer's disease (AD), and frontotemporal degeneration (FTD) in human and tau accumulation in preclinical tauopathy model, mice carrying P301S(L) tau mutation. The model describes tau production, tau-microtubules interaction, tau modification, aggregation and propagation through different brain compartments. Tau oligomers serve as the key intermediates for fibril growth and as mediators of tau pathology connectivity-driven propagation mediators from entorhinal cortex through limbic system to neocortex. The driver of londitudinal disease progression in AD model is the decrease of autophagic degradation of tau fibrils. For FTD case we use assumption about tau mutation influence on polymerization and microtubule binding confirmed by in vitro data. The human model versions were calibrated across published biochemical data for soluble, insoluble tau, CSF tau and PET (18F-AV-1451 SUVR) data. The mouse model was calibrated across biochemical data for soluble (RAB, RIPA soluble fractions), insoluble tau (FA soluble fraction) in several brain structures, interstitial fluid and CSF of mouse. Model satisfactorily describes consecutive appearance of insoluble tau in different regions corresponding to Braak stages of AD. FTD data verification revealed that tau propagation parameters differ from AD situation. Mouse model version satisfactorily describes reduction of soluble tau in brain and accumulation of insoluble tau in the mouse P301S(L). Tau pathology propagation and gradual appearance of polymerized tau starting from entorhinal cortex through the limbic system to neocortex is also predicted correctly and follows the experimentally observed trend. Data on immunotherapy by antibodies HJ8.5 in mouse are described satisfactorily only if significant (10 times) increase of insoluble tau degradation is assumed, while blocking tau propagation only does not allow for describing the mouse data. Analogous degradation activation in humans, would lead to complete disappearance of tau in AD, but cause moderate effect in FTD according to the model. The proposed QSP model could be considered as platform for investigation of therapeutic impact on tau pathology and translational analysis.

Quantitative Systems Pharmacology Modeling of Acid Sphingomyelinase Deficiency and the Enzyme Replacement Therapy Olipudase Alfa Is an Innovative Tool for Linking Pathophysiology and Pharmacology.

Acid sphingomyelinase deficiency (ASMD) is a rare lysosomal storage disorder with heterogeneous clinical manifestations, including hepatosplenomegaly and infiltrative pulmonary disease, and is associated with significant morbidity and mortality. Olipudase alfa (recombinant human acid sphingomyelinase) is an enzyme replacement therapy under development for the non‐neurological manifestations of ASMD. We present a quantitative systems pharmacology (QSP) model supporting the clinical development of olipudase alfa. The model is multiscale and mechanistic, linking the enzymatic deficiency driving the disease to molecular‐level, cellular‐level, and organ‐level effects. Model development was informed by natural history, and preclinical and clinical studies. By considering patient‐specific pharmacokinetic (PK) profiles and indicators of disease severity, the model describes pharmacodynamic (PD) and clinical end points for individual patients. The ASMD QSP model provides a platform for quantitatively assessing systemic pharmacological effects in adult and pediatric patients, and explaining variability within and across these patient populations, thereby supporting the extrapolation of treatment response from adults to pediatrics.

Improving the generation and selection of virtual populations in quantitative systems pharmacology models

Quantitative systems pharmacology (QSP) models aim to describe mechanistically the pathophysiology of disease and predict the effects of therapies on that disease. For most drug development applications, it is important to predict not only the mean response to an intervention but also the distribution of responses, due to inter-patient variability. Given the necessary complexity of QSP models, and the sparsity of relevant human data, the parameters of QSP models are often not well determined. One approach to overcome these limitations is to develop alternative virtual patients (VPs) and virtual populations (Vpops), which allow for the exploration of parametric uncertainty and reproduce inter-patient variability in response to perturbation. Here we evaluated approaches to improve the efficiency of generating Vpops. We aimed to generate Vpops without sacrificing diversity of the VPs’ pathophysiologies and phenotypes. To do this, we built upon a previously published approach (Allen et al., 2016) by (a) incorporating alternative optimization algorithms (genetic algorithm and Metropolis-Hastings) or alternatively (b) augmenting the optimized objective function. Each method improved the baseline algorithm by requiring significantly fewer plausible patients (precursors to VPs) to create a reasonable Vpop.

Prediction of Safety Margin and Optimization of Dosing Protocol for a Novel Antibiotic using Quantitative Systems Pharmacology Modeling.

Elevations of liver enzymes have been observed in clinical trials with BAL30072, a novel antibiotic. In vitro assays have identified potential mechanisms for the observed hepatotoxicity, including electron transport chain (ETC) inhibition and reactive oxygen species (ROS) generation. DILIsym, a quantitative systems pharmacology (QSP) model of drug‐induced liver injury, has been used to predict the likelihood that each mechanism explains the observed toxicity. DILIsym was also used to predict the safety margin for a novel BAL30072 dosing scheme; it was predicted to be low. DILIsym was then used to recommend potential modifications to this dosing scheme; weight‐adjusted dosing and a requirement to assay plasma alanine aminotransferase (ALT) daily and stop dosing as soon as ALT increases were observed improved the predicted safety margin of BAL30072 and decreased the predicted likelihood of severe injury. This research demonstrates a potential application for QSP modeling in improving the safety profile of candidate drugs.

Multiscale systems pharmacological analysis of everolimus action in hepatocellular carcinoma.

Dysregulation of mTOR pathway is common in hepatocellular carcinoma (HCC). A translational quantitative systems pharmacology (QSP), pharmacokinetic (PK), and pharmacodynamic (PD) model dissecting the circuitry of this pathway was developed to predict HCC patients' response to everolimus, an mTOR inhibitor. The time course of key signaling proteins in the mTOR pathway, HCC cells viability, tumor volume (TV) and everolimus plasma and tumor concentrations in xenograft mice, clinical PK of everolimus and progression free survival (PFS) in placebo and everolimus-treated patients were extracted from literature. A comprehensive and multiscale QSP/PK/PD model was developed, qualified, and translated to clinical settings. Model fittings and simulations were performed using Monolix software. The S6-kinase protein was identified as critical in the mTOR signaling pathway for describing everolimus lack of efficacy in HCC patients. The net growth rate constant (kg) of HCC cells was estimated at 0.02h-1 (2.88%RSE). The partition coefficient of everolimus into the tumor (kp) was determined at 0.06 (12.98%RSE). The kg in patients was calculated from the doubling time of TV in naturally progressing HCC patients, and was determined at 0.004day-1. Model-predicted and observed PFS were in good agreement for placebo and everolimus-treated patients. In conclusion, a multiscale QSP/PK/PD model elucidating everolimus lack of efficacy in HCC patients was successfully developed and predicted PFS reasonably well compared to observed clinical findings. This model may provide insights into clinical response to everolimus-based therapy and serve as a valuable tool for the clinical translation of efficacy for novel mTOR inhibitors.

Quantitative Systems Pharmacology Model of hUGT1A1-modRNA Encoding for the UGT1A1 Enzyme to Treat Crigler-Najjar Syndrome Type 1.

Crigler‐Najjar syndrome type 1 (CN1) is an autosomal recessive disease caused by a marked decrease in uridine‐diphosphate‐glucuronosyltransferase (UGT1A1) enzyme activity. Delivery of hUGT1A1‐modRNA (a modified messenger RNA encoding for UGT1A1) as a lipid nanoparticle is anticipated to restore hepatic expression of UGT1A1, allowing normal glucuronidation and clearance of bilirubin in patients. To support translation from preclinical to clinical studies, and first‐in‐human studies, a quantitative systems pharmacology (QSP) model was developed. The QSP model was calibrated to plasma and liver mRNA, and total serum bilirubin in Gunn rats, an animal model of CN1. This QSP model adequately captured the observed plasma and liver biomarker behavior across a range of doses and dose regimens in Gunn rats. First‐in‐human dose projections made using the translated model indicated that 0.5 mg/kg Q4W dose should provide a clinically meaningful and sustained reduction of >5 mg/dL in total bilirubin levels.

S221. QUANTITATIVE SYSTEMS PHARMACOLOGY AS AN ALTERNATIVE TO CHLORPROMAZINE EQUIVALENTS: PREDICTIVE VALIDATION FROM A CRIS DATABASE EXPERIMENT

BackgroundPolypharmacy is common in real clinical practice and in pharma-sponsored clinical trials. Chlorpromazine equivalents do not take into account pharmacodynamic interactions of drug combinations. If there is a sufficiently deep calibration set available, bio-informatics approaches can build classifiers for clinical phenotypes. However, this is not always the case which severely limits the generalizability of the predictions.MethodsWe applied a mechanism-based computer model of a cortico-striatal-thalamocortical loop of the dorsal motor circuit that has been calibrated with clinical data on the prevalence of extrapyramidal symptoms after antipsychotic treatment in schizophrenia patients and therapeutic interventions in Parkinson’s patients[1]. The Quantitative Systems Pharmacology (QSP) model is based on the appropriate connections between basal ganglia regions and consists of 220 neurons (8 different cell types), 3500 synapses and implementations of 32 CNS active targets, based on their unique locations and coupling with intracellular pathways. Modulation of the various CNS targets were calculated on simulating the competition between the endogenous neurotransmitter and the two drugs at their appropriate concentrations and affinity.The model was challenged to blindly predict the extrapyramidal symptoms liability of 1,124 patients prescribed two antipsychotics for six or more months (772 unique combinations). Anonymized data were derived from South London and Maudsley NHS Foundation Trust (SLAM) electronic health records (EHR). Extrapyramidal side effects were captured and identified using a combination of Natural Language Processing and a bespoke algorithm [2]. Only names and doses of the two drugs were made available without any calibration set.ResultsBlind prediction of the outcomes using a Receiver Operating Characteristic curve with the QSP model resulted in an Area-Under-the Curve of 0.64 (p<0.01), compared to an AUC of 0.52 for the sum of the chlorpromazine equivalents, 0.53 for the sum of affinity constants or the sum of D2R occupancies of the individual antipsychotics (AUC=0.52).DiscussionQSP is a powerful approach to predict PD-PD interactions in the absence of any calibration set or with limited and unique data and is superior to chlorpromazine equivalents for predicting EPS liability. A major application is the simulation of pharmacodynamic interactions of comedications in clinical trials with novel compounds leading to possible better balance between the different treatment arms

F189. PERSONALIZED MEDICINE: ESTIMATING THE IMPACT OF GENOTYPES ON ANTIPSYCHOTIC EFFICACY USING A QUANTITATIVE SYSTEMS PHARMACOLOGY APPROACH

BackgroundCNS disorders are lagging behind other indications such as oncology in implementing genotype-dependent treatment algorithms for personalized medicine. This is due to the limited knowledge about the interaction of the relevant biology and the drug’s pharmacologyMethodsWe applied a mechanism-based computer model of a cortico-striatal-thalamocortical loop of the dorsal motor circuit that has been calibrated with clinical data on antipsychotic treatment in schizophrenia patients (Spiros, Roberts et al. 2017). The Quantitative Systems Pharmacology (QSP) model is based on the appropriate connections between basal ganglia regions and consists of 220 neurons (8 different cell types), 3500 synapses and implementations of 32 CNS active targets, based on their unique locations and coupling with intracellular pathways. COMTVal156Met, 5-HTTLPR rs 23351 s/L and D2DRTaq1A1 genotypes are implemented using human imaging data in non-medicated human volunteersResultsThe dose-dependent antipsychotic effect for risperidone, aripiprazole and paliperidone is sensitive to the COMT genotype with the MM genotype having the greatest difference with the wild-type. Interestingly the 5-HTTLPR genotype interacts with the COMT genotype: this difference is positive for 5-HTTLPRss and negative for 5-HTTLPR LL. Olanzapine, quetiapine, clozapine and haloperidol are affected much less. The D2DRTaq1A allele interacts in a complex way with the COMT genotype with haloperidol, aripiprazole and risperidone and with the 5-HTLLPR genotype for haloperidol, aripiprazole, risperidone and paliperidone. These effects are anticipated to be detectable in clinical settings.DiscussionThe QSP platform predicts strong and complex genotype-dependent interactions with aripiprazole, risperidone, haloperidol and paliperidone and to a much smaller degree with olanzapine, quetiapine and clozapine. These predictions could in principle be verified in clinical setting and could lead to rational personalized treatment guidance

Model reduction in mathematical pharmacology

In this paper we present a framework for the reduction and linking of physiologically based pharmacokinetic (PBPK) models with models of systems biology to describe the effects of drug administration across multiple scales. To address the issue of model complexity, we propose the reduction of each type of model separately prior to being linked. We highlight the use of balanced truncation in reducing the linear components of PBPK models, whilst proper lumping is shown to be efficient in reducing typically nonlinear systems biology type models. The overall methodology is demonstrated via two example systems; a model of bacterial chemotactic signalling in Escherichia coli and a model of extracellular regulatory kinase activation mediated via the extracellular growth factor and nerve growth factor receptor pathways. Each system is tested under the simulated administration of three hypothetical compounds; a strong base, a weak base, and an acid, mirroring the parameterisation of pindolol, midazolam, and thiopental, respectively. Our method can produce up to an 80% decrease in simulation time, allowing substantial speed-up for computationally intensive applications including parameter fitting or agent based modelling. The approach provides a straightforward means to construct simplified Quantitative Systems Pharmacology models that still provide significant insight into the mechanisms of drug action. Such a framework can potentially bridge pre-clinical and clinical modelling - providing an intermediate level of model granularity between classical, empirical approaches and mechanistic systems describing the molecular scale.

Radiation and PD-(L)1 treatment combinations: immune response and dose optimization via a predictive systems model

BackgroundNumerous oncology combination therapies involving modulators of the cancer immune cycle are being developed, yet quantitative simulation models predictive of outcome are lacking. We here present a model-based analysis of tumor size dynamics and immune markers, which integrates experimental data from multiple studies and provides a validated simulation framework predictive of biomarkers and anti-tumor response rates, for untested dosing sequences and schedules of combined radiation (RT) and anti PD-(L)1 therapies.MethodsA quantitative systems pharmacology model, which includes key elements of the cancer immunity cycle and the tumor microenvironment, tumor growth, as well as dose-exposure-target modulation features, was developed to reproduce experimental data of CT26 tumor size dynamics upon administration of RT and/or a pharmacological IO treatment such as an anti-PD-L1 agent. Variability in individual tumor size dynamics was taken into account using a mixed-effects model at the level of tumor-infiltrating T cell influx.ResultsThe model allowed for a detailed quantitative understanding of the synergistic kinetic effects underlying immune cell interactions as linked to tumor size modulation, under these treatments. The model showed that the ability of T cells to infiltrate tumor tissue is a primary determinant of variability in individual tumor size dynamics and tumor response. The model was further used as an in silico evaluation tool to quantitatively predict, prospectively, untested treatment combination schedules and sequences. We demonstrate that anti-PD-L1 administration prior to, or concurrently with RT reveal further synergistic effects, which, according to the model, may materialize due to more favorable dynamics between RT-induced immuno-modulation and reduced immuno-suppression of T cells through anti-PD-L1.ConclusionsThis study provides quantitative mechanistic explanations of the links between RT and anti-tumor immune responses, and describes how optimized combinations and schedules of immunomodulation and radiation may tip the immune balance in favor of the host, sufficiently to lead to tumor shrinkage or rejection.

Systems pharmacological analysis of mitochondrial cardiotoxicity induced by selected tyrosine kinase inhibitors.

Tyrosine kinase inhibitors (TKIs) are targeted therapies rapidly becoming favored over conventional cytotoxic chemotherapeutics. Our study investigates two FDA approved TKIs, DASATINIB; indicated for IMATINIB-refractory chronic myeloid leukemia, and SORAFENIB; indicated for hepatocellular carcinoma and advanced renal cell carcinoma. Limited but crucial evidence suggests that these agents can have cardiotoxic side effects ranging from hypertension to heart failure. A greater understanding of the underlying mechanisms of this cardiotoxicity are needed as concerns grow and the capacity to anticipate them is lacking. The objective of this study wasto explore the mitochondrial-mediated cardiotoxic mechanisms of the two selected TKIs. This was achieved experimentally using immortalized human cardiomyocytes, AC16 cells, to investigate dose- and time-dependent cell killing, along with measurements of temporal changes in key signaling proteins involved in the intrinsic apoptotic and autophagy pathways upon exposure to these agents. Quantitative systems pharmacology (QSP) models were developed to capture the toxicological response in AC16 cells using protein dynamic data. The developed QSP models captured well all the various trends in protein signaling and cellular responses with good precision on the parameter estimates, and were successfully qualified using external data sets. An interplay between the apoptotic and autophagic pathways was identified to play a major role in determining toxicity associated with the investigated TKIs. The established modeling platform showed utility in elucidating the mechanisms of cardiotoxicity of SORAFENIB and DASATINIB. It may be useful for other small molecule targeted therapies demonstrating cardiac toxicities, and may aid in informing alternate dosing strategies to alleviate cardiotoxicity associated with these therapies.

Impact of amyloid-beta changes on cognitive outcomes in Alzheimer\u2019s disease: analysis of clinical trials using a quantitative systems pharmacology model

BackgroundDespite a tremendous amount of information on the role of amyloid in Alzheimer’s disease (AD), almost all clinical trials testing this hypothesis have failed to generate clinically relevant cognitive effects.MethodsWe present an advanced mechanism-based and biophysically realistic quantitative systems pharmacology computer model of an Alzheimer-type neuronal cortical network that has been calibrated with Alzheimer Disease Assessment Scale, cognitive subscale (ADAS-Cog) readouts from historical clinical trials and simulated the differential impact of amyloid-beta (Aβ40 and Aβ42) oligomers on glutamate and nicotinic neurotransmission.ResultsPreclinical data suggest a beneficial effect of shorter Aβ forms within a limited dose range. Such a beneficial effect of Aβ40 on glutamate neurotransmission in human patients is absolutely necessary to reproduce clinical data on the ADAS-Cog in minimal cognitive impairment (MCI) patients with and without amyloid load, the effect of APOE genotype effect on the slope of the cognitive trajectory over time in placebo AD patients and higher sensitivity to cholinergic manipulation with scopolamine associated with higher Aβ in MCI subjects. We further derive a relationship between units of Aβ load in our model and the standard uptake value ratio from amyloid imaging.When introducing the documented clinical pharmacodynamic effects on Aβ levels for various amyloid-related clinical interventions in patients with low Aβ baseline, the platform predicts an overall significant worsening for passive vaccination with solanezumab, beta-secretase inhibitor verubecestat and gamma-secretase inhibitor semagacestat. In contrast, all three interventions improved cognition in subjects with moderate to high baseline Aβ levels, with verubecestat anticipated to have the greatest effect (around ADAS-Cog value 1.5 points), solanezumab the lowest (0.8 ADAS-Cog value points) and semagacestat in between. This could explain the success of many amyloid interventions in transgene animals with an artificial high level of Aβ, but not in AD patients with a large variability of amyloid loads.ConclusionsIf these predictions are confirmed in post-hoc analyses of failed clinical amyloid-modulating trials, one should question the rationale behind testing these interventions in early and prodromal subjects with low or zero amyloid load.

Preclinical QSP Modeling in the Pharmaceutical Industry: An IQ Consortium Survey Examining the Current Landscape.

A cross‐industry survey was conducted to assess the landscape of preclinical quantitative systems pharmacology (QSP) modeling within pharmaceutical companies. This article presents the survey results, which provide insights on the current state of preclinical QSP modeling in addition to future opportunities. Our results call attention to the need for an aligned definition and consistent terminology around QSP, yet highlight the broad applicability and benefits preclinical QSP modeling is currently delivering.

Integrated quantitative systems pharmacology (QSP) model of lysosomal diseases provides an innovative computational platform to support research and therapeutic development for the sphingolipidoses

Integrated quantitative systems pharmacology (QSP) model of lysosomal diseases provides an innovative computational platform to support research and therapeutic development for the sphingolipidoses

Boolean network modeling in systems pharmacology.

Quantitative systems pharmacology (QSP) is an emerging discipline that aims to discover how drugs modulate the dynamics of biological components in molecular and cellular networks and the impact of those perturbations on human pathophysiology. The integration of systems-based experimental and computational approaches is required to facilitate the advancement of this field. QSP models typically consist of a series of ordinary differential equations (ODE). However, this mathematical framework requires extensive knowledge of parameters pertaining to biological processes, which is often unavailable. An alternative framework that does not require knowledge of system-specific parameters, such as Boolean network modeling, could serve as an initial foundation prior to the development of an ODE-based model. Boolean network models have been shown to efficiently describe, in a qualitative manner, the complex behavior of signal transduction and gene/protein regulatory processes. In addition to providing a starting point prior to quantitative modeling, Boolean network models can also be utilized to discover novel therapeutic targets and combinatorial treatment strategies. Identifying drug targets using a network-based approach could supplement current drug discovery methodologies and help to fill the innovation gap across the pharmaceutical industry. In this review, we discuss the process of developing Boolean network models and the various analyses that can be performed to identify novel drug targets and combinatorial approaches. An example for each of these analyses is provided using a previously developed Boolean network of signaling pathways in multiple myeloma. Selected examples of Boolean network models of human (patho-)physiological systems are also reviewed in brief.

Frequency-Domain Response Analysis for Quantitative Systems Pharmacology Models.

Drug dosing regimen can significantly impact drug effect and, thus, the success of treatments. Nevertheless, trial and error is still the most commonly used method by conventional pharmacometric approaches to optimize dosing regimen. In this tutorial, we utilize four distinct classes of quantitative systems pharmacology models to introduce frequency‐domain response analysis, a method widely used in electrical and control engineering that allows the analytical optimization of drug treatment regimen from the dynamics of the model.

Selection and Qualification of Simplified QSP Models When Using Model Order Reduction Techniques.

Quantitative systems pharmacology (QSP) models are increasingly used in drug development to provide a deep understanding of the mechanism of action of drugs and to identify appropriate disease targets. Such models are, however, not suitable for estimation purposes due to their high dimensionality. Based on any desired and specific input-output relationship, the system may be reduced to a model with fewer states and parameters. However, any simplification process will be a trade-off between model performance and complexity. In this study, we develop a weighted composite criterion which brings together the opposing indices of performance and dimensionality. The weighting factor can be determined by qualification of the simplified model based on a visual predictive check (VPC) using the precision of each parameter. The weighted criterion and model qualification techniques were illustrated with three examples: a simple compartmental pharmacokinetic model, a physiologically based pharmacokinetic (PBPK) example, and a semimechanistic model for bone mineral density. When considering the PBPK example, this automated search identified the same reduced model which had been detected in a previous report, as well as a simpler model which had not been previously identified. The simpler bone mineral density model provided an adequate description of the response even after 1year from the initiation of treatment. The proposed criterion together with a VPC provides a natural way for model order reduction that can be fully automated and applied to multiscale models.

Multiscale Mathematical Model of Drug-Induced Proximal Tubule Injury: Linking Urinary Biomarkers to Epithelial Cell Injury and Renal Dysfunction.

Drug-induced nephrotoxicity is a major cause of acute kidney injury, and thus detecting the potential for nephrotoxicity early in the drug development process is critical. Various urinary biomarkers exhibit different patterns following drug-induced injury, which may provide greater information than traditional biomarkers like serum creatinine. In this study, we developed a multiscale quantitative systems pharmacology model relating drug exposure to proximal tubule (PT) epithelial cell injury and subsequently to expression of multiple urinary biomarkers and organ-level functional changes. We utilized urinary kidney injury molecule-1 (Kim-1), alpha glutathione S-transferase, albumin (αGST), glucose, and urine volume time profiles as well as serum creatinine and histopathology data obtained from rats treated with the nephrotoxicant cisplatin to develop the model. Although the model was developed using single-dose response to cisplatin, the model predicted the serum creatinine response to multidose cisplatin regimens. Further, using only the urinary Kim-1 response to gentamicin (a nephrotoxicant with a distinctly different injury time course than cisplatin), the model detected and predicted mild to moderate PT injury, as confirmed with histopathology, even when serum creatinine was unchanged. Thus, the model is generalizable, and can be used to deconvolute the underlying degree and time course of drug-induced PT injury and renal dysfunction from a small number of urinary biomarkers, and may provide a tool to determine optimal dosing regimens that minimize renal injury.