Development of a stochastic model for the efficacy of NRTIs using known mechanisms of action

Top-down and bottom-up modeling in system pharmacology to understand clinical efficacy: An example with NRTIs of HIV-1

A major aim of Systems Pharmacology is to understand clinically relevant mechanisms of action (MOA) of drugs and to use this knowledge in order to optimize therapy. To enable this mission it is necessary to obtain knowledge on how in vitro testable insights translate into clinical efficacy. Mathematical modeling and data integration are essential components to achieve this goal. Two modeling philosophies are prevalent, each of which in isolation is not sufficient to achieve the above described: In a 'top-down' approach, a minimal pharmacokinetic-pharmacodynamic (PK-PD) model is derived from- and fitted to available clinical data. This model may lack interpretability in terms of mechanisms and may only be predictive for scenarios already covered by the data used to derive it. A 'bottom-up' approach builds on mechanistic insights derived from in vitro/ex vivo experiments, which can be conducted under controlled conditions, but may not be fully representative for the in vivo/clinical situation. In this work, we employ both approaches side-by-side to predict the clinical potency (IC₅₀ values) of the nucleoside reverse transcriptase inhibitors (NRTIs) lamivudine, emtricitabine and tenofovir. In the 'top-down' approach, this requires to establish the dynamic link between the intracellularly active NRTI-triphosphates (which exert the effect) and plasma prodrug PK and to subsequently link this composite PK model to viral kinetics. The 'bottom-up' approach assesses inhibition of reverse transcriptase-mediated viral DNA polymerization by the intracellular, active NRTI-triphosphates, which has to be brought into the context of target cell infection. By using entirely disparate sets of data to derive and parameterize the respective models, our approach serves as a means to assess the clinical relevance of the 'bottom-up' approach. We obtain very good qualitative and quantitative agreement between 'top-down' vs. 'bottom-up' predicted IC₅₀ values, arguing for the validity of the 'bottom-up' approach. We noted, however, that the 'top-down' approach is strongly dependent on the sparse and noisy intracellular pharmacokinetic data. All in all, our work provides confidence that we can translate in vitro parameters into measures of clinical efficacy using the 'bottom-up' approach. This may allow to infer the potency of various NRTIs in inhibiting e.g. mutant viruses, to distinguish sources of interaction of NRTI combinations and to assess the efficacy of different NRTIs for repurposing, e.g. for pre-exposure prophylaxis.

Towards structural systems pharmacology to study complex diseases and personalized medicine

Genome-Wide Association Studies (GWAS), whole genome sequencing, and high-throughput omics techniques have generated vast amounts of genotypic and molecular phenotypic data. However, these data have not yet been fully explored to improve the effectiveness and efficiency of drug discovery, which continues along a one-drug-one-target-one-disease paradigm. As a partial consequence, both the cost to launch a new drug and the attrition rate are increasing. Systems pharmacology and pharmacogenomics are emerging to exploit the available data and potentially reverse this trend, but, as we argue here, more is needed. To understand the impact of genetic, epigenetic, and environmental factors on drug action, we must study the structural energetics and dynamics of molecular interactions in the context of the whole human genome and interactome. Such an approach requires an integrative modeling framework for drug action that leverages advances in data-driven statistical modeling and mechanism-based multiscale modeling and transforms heterogeneous data from GWAS, high-throughput sequencing, structural genomics, functional genomics, and chemical genomics into unified knowledge. This is not a small task, but, as reviewed here, progress is being made towards the final goal of personalized medicines for the treatment of complex diseases.

HIV-1 polymerase inhibition by nucleoside analogs: cellular- and kinetic parameters of efficacy, susceptibility and resistance selection

Nucleoside analogs (NAs) are used to treat numerous viral infections and cancer. They compete with endogenous nucleotides (dNTP/NTP) for incorporation into nascent DNA/RNA and inhibit replication by preventing subsequent primer extension. To date, an integrated mathematical model that could allow the analysis of their mechanism of action, of the various resistance mechanisms, and their effect on viral fitness is still lacking. We present the first mechanistic mathematical model of polymerase inhibition by NAs that takes into account the reversibility of polymerase inhibition. Analytical solutions for the model point out the cellular- and kinetic aspects of inhibition. Our model correctly predicts for HIV-1 that resistance against nucleoside analog reverse transcriptase inhibitors (NRTIs) can be conferred by decreasing their incorporation rate, increasing their excision rate, or decreasing their affinity for the polymerase enzyme. For all analyzed NRTIs and their combinations, model-predicted macroscopic parameters (efficacy, fitness and toxicity) were consistent with observations. NRTI efficacy was found to greatly vary between distinct target cells. Surprisingly, target cells with low dNTP/NTP levels may not confer hyper-susceptibility to inhibition, whereas cells with high dNTP/NTP contents are likely to confer natural resistance. Our model also allows quantification of the selective advantage of mutations by integrating their effects on viral fitness and drug susceptibility. For zidovudine triphosphate (AZT-TP), we predict that this selective advantage, as well as the minimal concentration required to select thymidine-associated mutations (TAMs) are highly cell-dependent. The developed model allows studying various resistance mechanisms, inherent fitness effects, selection forces and epistasis based on microscopic kinetic data. It can readily be embedded in extended models of the complete HIV-1 reverse transcription process, or analogous processes in other viruses and help to guide drug development and improve our understanding of the mechanisms of resistance development during treatment.

Nucleoside analogs (NAs) represent an important drug class for the treatment of viral infections and cancer. They inhibit DNA/RNA polymerization after being incorporated into nascent DNA/RNA, which prevents primer extension. Viruses are particularly versatile and frequently develop mutations enabling them to avert the effects of NAs. The mechanisms of resistance development are, however, still poorly understood. Through mathematical modeling, we assess the mechanisms by which HIV-1 can develop resistance against nucleoside analog reverse transcriptase inhibitors (NRTI). We quantify the effects of treatment and estimate the fitness of drug resistant mutants. We correctly predict that HIV-1 can develop resistance by decreasing NRTI incorporation rate, increasing its excision rate, or decreasing its affinity for the viral polymerase enzyme. Our model also allows quantification of the cell specific factors affecting NRTI efficacy. Resistance development also changes drug susceptibility distinctly and we show, for the first time, that selection of drug resistance can occur in particular target cells. This finding could provide an explanation of how clinically observed resistant viral mutants may arise. It also pin-points important parameters that may impact clinical efficacy of NAs used to treat other viruses.

Novel computational approaches to polypharmacology as a means to define responses to individual drugs

Polypharmacology, which focuses on designing therapeutics to target multiple receptors, has emerged as a new paradigm in drug discovery. Polypharmacological effects are an attribute of most, if not all, drug molecules. The efficacy and toxicity of drugs, whether designed as single- or multitarget therapeutics, result from complex interactions between pharmacodynamic, pharmacokinetic, genetic, epigenetic, and environmental factors. Ultimately, to predict a drug response phenotype, it is necessary to understand the change in information flow through cellular networks resulting from dynamic drug-target interactions and the impact that this has on the complete biological system. Although such is a future objective, we review recent progress and challenges in computational techniques that enable the prediction and analysis of in vitro and in vivo drug-response phenotypes.