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Purpose: Ceralasertib is a potent and selective oral inhibitor of the serine/threonine protein kinase ataxia telangiectasia and Rad3-related (ATR) protein. Patients and Methods: Eligible patients with solid tumors, enriched for melanoma, received ceralasertib in combination with a fixed dose of paclitaxel (80 mg/m2 on D1, D8, D15) in 28-day cycles. The dose of ceralasertib was escalated to reach an MTD in a rolling 6 design. The starting dose of ceralasertib was 40 mg QD. Fifty-seven patients (33 patients with melanoma who failed prior PD1/L1 treatment) were enrolled in 7 dose cohorts ranging from 40 mg QD to 240 mg BD plus weekly paclitaxel. Results: The RP2D was established as ceralasertib 240 mg BD days 1–14 plus paclitaxel 80 mg/m2 on D1, D8, D15 every 28 days. The most common toxicities were neutropenia (n = 39, 68%), anemia (n = 25, 44%), and thrombocytopenia (n = 21, 37%). In the full analysis set of 57 patients, the overall response rate (ORR) was 22.6% (95% CI, 12.5–35.3). In 33 patients with melanoma, resistant to prior anti-PD1 therapy, the ORR was 33.3% (95% CI, 18.0–51.8). In the melanoma subset, the mPFS was 3.6 months (95% CI, 2.0–5.8), the median duration of response was 9.9 months (95% CI, 3.7–23.2), and the mOS was 7.4 months (95% CI, 5.7–11.9). Conclusions: Ceralasertib in combination with paclitaxel was well tolerated in patients with advanced malignancies and showed evidence of antitumor activity. Durable responses were observed in patients with advanced cutaneous, acral, and mucosal melanoma resistant to anti-PD1/L1 treatment. See related commentary by Ashworth, p. 4667

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., Highlights 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 in vitro 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.

Drug development in Systemic Lupus Erythematosus (SLE) has been hindered by poor translation from successful preclinical experiments to clinical efficacy. This lack of success has been attributed to the high heterogeneity of SLE patients and to the lack of understanding of disease physiopathology. Modelling approaches could be useful for supporting the identification of targets, biomarkers and patient subpopulations with differential response to drugs. However, the use of traditional quantitative models based on differential equations is not justifiable in a sparse data situation. Boolean networks models are less demanding on the required data to be implemented and can provide insights into the dynamics of biological networks. This methodology allows the integration of all the available knowledge into a single framework to evaluate the behavior of the system under different conditions and test hypotheses about unknown aspects of the disease. In this proof-of-concept study, we explored the potential of a systems pharmacology model based on Boolean networks to support drug development in SLE. We focused the analysis on the antigen presentation by the antigen presenting cells (APC) to the T-cells to evaluate the scope of this methodology in a medium size network before full implementation of the whole SLE pathway. The heterogeneity of SLE patients was replicated using this methodology simulating subjects with distinct pathway alterations. A perturbation analysis of the network coupled with clustering analysis showed potential to identify drug targets, optimal combinatorial regimens and subpopulations of responders and non-responders to drug treatment. We propose this approach as a first step towards the development of more quantitative platforms to address the current challenges in drug development for complex diseases.

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., Author summary Mathematical models of the disease processes are widely used in computational biology to quantitatively describe the time course of disease progression and are often linked to pharmacokinetic–pharmacodynamic models in order to evaluate the effect of drug treatment on disease. Once the models are built from observed information and/or literature data, they can predict the dynamics of the system under different conditions through computer simulations. However, simulation exercises are not always effective to obtain the desired objectives due to the complexity of these systems. In this work, we optimized the release characteristics of a synthetic gonadotropin-releasing hormone analog used to induce chemical castration by inhibiting the testosterone levels in prostate cancer patients. The therapeutic goals to achieve were to minimize the initial flare up of testosterone levels and the time to reach testosterone values below castration limit, while maximizing the castration period of the patients. Our methodology, based on control theory, introduces a manipulable variable into the system’s equations to drive the model towards the established goals. We demonstrated how drug-release properties can be improved with the implementation of optimal control strategies to enhance the outcome of cancer patients. These methods are extrapolable to other problems encountered in the field.

Motivation : 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. Results : 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-TNF alpha, 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-IFN gamma 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.

Motivation 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. Results 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. Availability and Implementation The source code is available at https://github.com/SPIDDOR/SPIDDOR. Supplementary information Supplementary data are available at Bioinformatics online.