Immune checkpoint inhibitors (ICIs) have demonstrated significant clinical impact in improving overall survival of several malignancies associated with poor outcomes; however, only 20–40% of patients ...will show long-lasting survival. Further clarification of factors related to treatment response can support improvements in clinical outcome and guide the development of novel immune checkpoint therapies. In this article, we have provided an overview of the pharmacokinetic (PK) aspects related to current ICIs, which include target-mediated drug disposition and time-varying drug clearance. In response to the variation in treatment exposure of ICIs and the significant healthcare costs associated with these agents, arguments for both dose individualization and generalization are provided. We address important issues related to the efficacy and safety, the pharmacodynamics (PD), of ICIs, including exposure–response relationships related to clinical outcome. The unique PK and PD aspects of ICIs give rise to issues of confounding and suboptimal surrogate endpoints that complicate interpretation of exposure–response analysis. Biomarkers to identify patients benefiting from treatment with ICIs have been brought forward. However, validated biomarkers to monitor treatment response are currently lacking.
Numerous problems encountered in computational biology can be formulated as optimization problems. In this context, optimization of drug release characteristics or dosing schedules for anticancer ...agents has become a prominent area not only for the development of new drugs, but also for established drugs. However, in complex systems, optimization of drug exposure is not a trivial task and cannot be efficiently addressed through trial-error simulation exercises. Finding a solution to those problems is a challenging task which requires more advanced strategies like optimal control theory. In this work, we perform an optimal control analysis on a previously developed computational model for the testosterone effects of triptorelin in prostate cancer patients with the goal of finding optimal drug-release characteristics. We demonstrate how numerical control optimization of non-linear models can be used to find better therapeutic approaches in order to improve the final outcome of the patients.
The model-informed drug discovery and development paradigm is now well established among the pharmaceutical industry and regulatory agencies. This success has been mainly due to the ability of ...pharmacometrics to bring together different modeling strategies, such as population pharmacokinetics/pharmacodynamics (PK/PD) and systems biology/pharmacology. However, there are promising quantitative approaches that are still seldom used by pharmacometricians and that deserve consideration. One such case is the stochastic modeling approach, which can be important when modeling small populations because random events can have a huge impact on these systems. In this review, we aim to raise awareness of stochastic models and how to combine them with existing modeling techniques, with the ultimate goal of making future drug–disease models more versatile and realistic.
Nonlinear mixed-effect modeling is currently the most successful methodology used to characterize the PK/PD data from different individuals.Stochastic modeling approaches deserve consideration for use in model-informed drug discovery and development because events occurring at random can have important repercussions on disease progression and treatment effects, especially when population size is small.Stochastic models can also help in the refinement of the original structural PK/PD model.Deterministic systems have a greater mathematical simplicity, are computationally less demanding, and parameter estimation is well established with a large existing toolkit to help with model fitting and simulation.Coronavirus disease 2019 is the most recent application where researchers are building such models to describe the spread of this disease in the population.
Identification of optimal schedules for combination drug administration relies on accurately estimating the correct pharmacokinetics, pharmacodynamics, and drug interaction effects. Misspecification ...of pharmacokinetics can lead to wrongly predicted timing or order of treatments, leading to schedules recommended on the basis of incorrect assumptions about absorption and elimination of a drug and its effect on tumor growth. Here, we developed a computational modeling platform and software package for combination treatment strategies with flexible pharmacokinetic profiles and multidrug interaction curves that are estimated from data. The software can be used to compare prespecified schedules on the basis of the number of resistant cells where drug interactions and pharmacokinetic curves can be estimated from user-provided data or models. We applied our approach to publicly available
data of treatment with different tyrosine kinase inhibitors of BT-20 triple-negative breast cancer cells and of treatment with erlotinib of PC-9 non-small cell lung cancer cells. Our approach is publicly available in the form of an R package called ACESO (https://github.com/Michorlab/aceso) and can be used to investigate optimum dosing for any combination treatment. SIGNIFICANCE: These findings introduce a computational modeling platform and software package for combination treatment strategies with flexible pharmacokinetic profiles and multidrug interaction curves that are estimated from data.
Mathematical modeling of unperturbed and perturbed tumor growth dynamics (TGD) in preclinical experiments provides an opportunity to establish translational frameworks. The most commonly used ...unperturbed tumor growth models (i.e. linear, exponential, Gompertz and Simeoni) describe a monotonic increase and although they capture the mean trend of the data reasonably well, systematic model misspecifications can be identified. This represents an opportunity to investigate possible underlying mechanisms controlling tumor growth dynamics through a mathematical framework. The overall goal of this work is to develop a data-driven semi-mechanistic model describing non-monotonic tumor growth in untreated mice. For this purpose, longitudinal tumor volume profiles from different tumor types and cell lines were pooled together and analyzed using the population approach. After characterizing the oscillatory patterns (oscillator half-periods between 8–11 days) and confirming that they were systematically observed across the different preclinical experiments available (p<10
−9
), a tumor growth model was built including the interplay between resources (i.e. oxygen or nutrients), angiogenesis and cancer cells. The new structure, in addition to improving the model diagnostic compared to the previously used tumor growth models (i.e. AIC reduction of 71.48 and absence of autocorrelation in the residuals (p>0.05)), allows the evaluation of the different oncologic treatments in a mechanistic way. Drug effects can potentially, be included in relevant processes taking place during tumor growth. In brief, the new model, in addition to describing non-monotonic tumor growth and the interaction between biological factors of the tumor microenvironment, can be used to explore different drug scenarios in monotherapy or combination during preclinical drug development.
The literature on complex diseases is abundant but not always quantitative. This is particularly so for Inflammatory Bowel Disease (IBD), where many molecular pathways are qualitatively well ...described but this information cannot be used in traditional quantitative mathematical models employed in drug development. We propose the elaboration and validation of a logic network for IBD able to capture the information available in the literature that will facilitate the identification/validation of therapeutic targets.
In this article, we propose a logic model for Inflammatory Bowel Disease (IBD) which consists of 43 nodes and 298 qualitative interactions. The model presented is able to describe the pathogenic mechanisms of the disorder and qualitatively describes the characteristic chronic inflammation. A perturbation analysis performed on the IBD network indicates that the model is robust. Also, as described in clinical trials, a simulation of anti-TNFα, anti-IL2 and Granulocyte and Monocyte Apheresis showed a decrease in the Metalloproteinases node (MMPs), which means a decrease in tissue damage. In contrast, as clinical trials have demonstrated, a simulation of anti-IL17 and anti-IFNγ or IL10 overexpression therapy did not show any major change in MMPs expression, as corresponds to a failed therapy. The model proved to be a promising in silico tool for the evaluation of potential therapeutic targets, the identification of new IBD biomarkers, the integration of IBD polymorphisms to anticipate responders and non-responders and can be reduced and transformed in quantitative model/s.
Lurbinectedin is an inhibitor of RNA polymerase II currently under clinical development for intravenous administration as a single agent and in combination with other anti-tumor agents for the ...treatment of several tumor types. The objective of this work was to develop a population-pharmacokinetic model in this patient setting and to elucidate the main predictors to guide the late stages of development.
Data from 443 patients with solid and hematologic malignancies treated in six phase I and three phase II trials with lurbinectedin as a single agent or combined with other agents were included in the analysis. The potential influence of demographic, co-treatment, and laboratory characteristics on lurbinectedin pharmacokinetics was evaluated.
The final population-pharmacokinetic model was an open three-compartment model with linear distribution and linear elimination from the central compartment. Population estimates for total plasma clearance, and apparent volume at steady state were 11.2 L/h and 438 L, respectively. Inter-individual variability was moderate for all parameters, ranging from 20.9 to 51.2%. High α-1-acid glycoprotein and C-reactive protein, and low albumin reduced clearance by 28, 20, and 20%, respectively. Co-administration of cytochrome P450 3A inhibitors reduced clearance by 30%. Combinations with other anti-tumor agents did not modify the pharmacokinetics of lurbinectedin significantly.
The population-pharmacokinetic model indicated neither a dose nor time dependency, and no clinically meaningful pharmacokinetic differences were found when co-administered with other anticancer agents. A chronic inflammation pattern characterized by decreased albumin and increased C-reactive protein and α-1-acid glycoprotein levels led to high lurbinectedin exposure. Co-administration of cytochrome P450 3A inhibitors increased lurbinectedin exposure.
•A QSP model characterizing VPA-mediated hyperammonemia was developed and verified in its key elements.•The QSP model incorporated a PopPK model describing total and unbound VPA levels after single ...and multiple doses.•L-carnitine supplementation is predicted to prevent hyperammonemia under both chronic treatment and after overdosing with VPA.
Valproic acid (VPA) is a short-chain fatty acid widely prescribed in the treatment of seizure disorders and epilepsy syndromes, although its therapeutic value may be undermined by its toxicity. VPA serious adverse effects are reported to have a significant and dose-dependent incidence, many associated with VPA-induced hyperammonemia. This effect has been linked with reduced levels of carnitine; an endogenous compound involved in fatty acid's mitochondrial β-oxidation by facilitation of its entrance via the carnitine shuttle. High exposure to VPA can lead to carnitine depletion causing a misbalance between the intra-mitochondrial β-oxidation and the microsomal ω-oxidation, a pathway that produces toxic metabolites such as 4-en-VPA which inhibits ammonia elimination. Moreover, a reduction in carnitine levels might be also related to VPA-induced obesity and lipids disorder. In turn, L-carnitine supplementation (CS) has been recommended and empirically used to reduce VPA's hepatotoxicity. The aim of this work was to develop a Quantitative Systems Pharmacology (QSP) model to characterize VPA-induced hyperammonemia and evaluate the benefits of CS in preventing hyperammonemia under both chronic treatment and after VPA overdosing. The QSP model included a VPA population pharmacokinetics model that allowed the prediction of total and unbound concentrations after single and multiple oral doses considering its saturable binding to plasma proteins. Predictions of time courses for 2-en-VPA, 4-en-DPA, VPA-glucuronide, carnitine, ammonia and urea levels, and for the relative change in fatty acids, Acetyl-CoA, and glutamate reflected the VPA induced changes and the efficacy of the treatment with L-carnitine. The QSP model was implemented to give a rational basis for the L-carnitine dose selection to optimize CS depending on VPA dosage regime and to assess the currently recommended L-carnitine rescue therapy after VPA overdosing. Results show that a L-carnitine dose equal to the double of the VPA dose using the same interdose interval would maintain the ammonia levels at baseline. The QSP model may be expanded in the future to describe other adverse events linked to VPA-induced changes in endogenous compounds.
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Immuno-oncology (IO) focuses on the ability of the immune system to detect and eliminate cancer cells. Since the approval of the first immune checkpoint inhibitor, immunotherapies have become a major ...player in oncology treatment and, in 2021, represented the highest number of approved drugs in the field. In spite of this, there is still a fraction of patients that do not respond to these therapies and develop resistance mechanisms. In this sense, mathematical models offer an opportunity to identify predictive biomarkers, optimal dosing schedules and rational combinations to maximize clinical response. This work aims to outline the main therapeutic targets in IO and to provide a description of the different mathematical approaches (top-down, middle-out, and bottom-up) integrating the cancer immunity cycle with immunotherapeutic agents in clinical scenarios. Among the different strategies, middle-out models, which combine both theoretical and evidence-based description of tumor growth and immunological cell-type dynamics, represent an optimal framework to evaluate new IO strategies.
Literature on complex diseases is abundant but not always quantitative. Many molecular pathways are qualitatively well described but this information cannot be used in traditional quantitative ...mathematical models employed in drug development. Tools for analysis of discrete networks are useful to capture the available information in the literature but have not been efficiently integrated by the pharmaceutical industry. We propose an expansion of the usual analysis of discrete networks that facilitates the identification/validation of therapeutic targets.
In this article, we propose a methodology to perform Boolean modeling of Systems Biology/Pharmacology networks by using SPIDDOR (Systems Pharmacology for effIcient Drug Development On R) R package. The resulting models can be used to analyze the dynamics of signaling networks associated to diseases to predict the pathogenesis mechanisms and identify potential therapeutic targets.
The source code is available at https://github.com/SPIDDOR/SPIDDOR .
itzirurzun@alumni.unav.es , itroconiz@unav.es.
Supplementary data are available at Bioinformatics online.
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