Multi-scale modeling of drug binding kinetics to predict drug efficacy

Abstract

Optimizing drug therapies for any disease requires a solid understanding of pharmacokinetics (the drug concentration at a given time point in different body compartments) and pharmacodynamics (the effect a drug has at a given concentration). Mathematical models are frequently used to infer drug concentrations over time based on infrequent sampling and/or in inaccessible body compartments. Models are also used to translate drug action from in vitro to in vivo conditions or from animal models to human patients. Recently, mathematical models that incorporate drug-target binding and subsequent downstream responses have been shown to advance our understanding and increase predictive power of drug efficacy predictions. We here discuss current approaches of modeling drug binding kinetics that aim at improving model-based drug development in the future. This in turn might aid in reducing the large number of failed clinical trials.

Multi-step vs. single-step resistance evolution under different drugs, pharmacokinetics and treatment regimens

“…We predicted the emergence of mutations in the population by using a pharmacodynamic model, which connects bacterial growth (or reductions thereof) to antimicrobial drug concentration via a Hill function (Fig. 1A) 1,5–7,11,57 . Benefits and costs were taken from the positive correlation that was observed with literature values (slope = 0.0087) - except for simulations testing the dependence of our results on this relationship, where we took a steeper correlation (slope = 0.0467) (Fig.…”

“…The choice of treatment strategy can significantly determine the efficacy of pathogen removal and the potential for resistance evolution 1–3 , highlighting the importance of careful consideration of drug type, dose and duration 1,4 . In order to deter drug resistance and preserve drug efficacy, treatment strategies should be guided by a predictive understanding of resistance evolution dynamics 5,6 – a task that is substantially facilitated through mathematical modeling 1,5–7 . One severely understudied aspect in such approaches is that there are two fundamentally different patterns of de novo antibiotic resistance evolution 8 : i) ‘single-step’ resistance: a single mutation provides higher drug resistance than a given treatment dose, or ii) ‘multi-step’ resistance: the accumulation of several mutations of low individual benefit is necessary for high-level resistance.…”

“…The main class of models used to predict drug action are pharmacokinetic and pharmacodynamic (PKPD) models 7–10 , which describe the change in drug concentration over time (pharmacokinetics) and the corresponding effect on a pathogen population (pharmacodynamics). PKPD approaches are most commonly employed to study the efficacy of treatment without considering the possibility of resistance evolution, but coupled with bacterial population models, they can be used to investigate drug resistance evolution over time 11 .…”

vCOMBAT: a Novel Tool to Create and Visualize a COmputational Model of Bacterial Antibiotic Target-binding

“…In this section, we compare the output of our vCOMBAT model -a mechanistic pharmacodynamic model with the traditional pharmacodynamic model by Aljayyoussi et al [31]. Mechanistic models provide a deep understanding of drug action and capture various pharmacodynamic effects [16]. Traditional models, on the other hand, are simpler but limited due to several assumptions that are likely invalid in reality.…”

“…We notice that for a single-dose treatment (600 mg of Rifampicin) with the vCOMBAT model, the total bacteria population reduces for two days before bacteria regrow while with the traditional model, the population decreases and then increases after approximately 18 hours. This can be explained by the post-antibiotic effect [16] which the mechanistic models can capture. The post-antibiotic effect is the delay of the bacterial regrowth after bacteria are exposed to antibiotics.…”