Interrogating and Quantifying In Vitro Cancer Drug Pharmacodynamics via Agent-Based and Bayesian Monte Carlo Modelling

Abstract

The effectiveness of chemotherapy in cancer cell regression is often limited by drug resistance, toxicity, and neoplasia heterogeneity. However, due to the significant complexities entailed by the many cancer growth processes, predicting the impact of interference and symmetry-breaking mechanisms is a difficult problem. To quantify and understand more about cancer drug pharmacodynamics, we combine in vitro with in silico cancer models. The anti-proliferative action of selected cytostatics is interrogated on human colorectal and breast adenocarcinoma cells, while an agent-based computational model is employed to reproduce experiments and shed light on the main therapeutic mechanisms of each chemotherapeutic agent. Multiple drug administration scenarios on each cancer cell line are simulated by varying the drug concentration, while a Bayesian-based method for model parameter optimisation is employed. Our proposed procedure of combining in vitro cancer drug screening with an in silico agent-based model successfully reproduces the impact of chemotherapeutic drugs in cancer growth behaviour, while the mechanisms of action of each drug are characterised through model-derived probabilities of cell apoptosis and division. We suggest that our approach could form the basis for the prospective generation of experimentally-derived and model-optimised pharmacological variables towards personalised cancer therapy.

Combined Biological and Numerical Modeling Approach for Better Understanding of the Cancer Viability and Apoptosis

“…Depending on the used cell model system, we applied precisely determined chemotherapeutics that are used in clinical practice for the treatment of cancer. In our earlier research, in a similar study, we relied on the processing of cell viability results obtained by a standard biochemical method, where we monitored the reduction of yellow MTT to purple formazan crystals for tracking the metabolic activity of viable cells to estimate the number of cells that survived after drug treatment [ 31 ]. In this study, we used a far more sophisticated RTCA method to monitor cell viability at a much larger number of time points in (real) time.…”

“…Treatment concentrations in flow cytometry and qPCR experimentation were used as such—lower concentrations not to be significantly cytotoxic, and higher concentrations to exert cytotoxicity. Our previous results [ 31 ] on the cytotoxicity of these chemotherapeutics enabled us to choose these two concentrations. For example, doxorubicin treatment concentrations were significantly lower because of extreme toxicity on MDA-MB-231 cells—IC 50 24h = 37.9 µM, IC 50 72h = 0.2 µM [ 31 ].…”

“…Our previous results [ 31 ] on the cytotoxicity of these chemotherapeutics enabled us to choose these two concentrations. For example, doxorubicin treatment concentrations were significantly lower because of extreme toxicity on MDA-MB-231 cells—IC 50 24h = 37.9 µM, IC 50 72h = 0.2 µM [ 31 ]. RTCA treatment concentrations for doxorubicin were 0.1, 1, 10, and 50 µM, while for flow cytometry and qPCR they were 0.1 and 1 µM.…”

Agent-based modeling in cancer biomedicine: applications and tools for calibration and validation

“…ML can help address this problem. For instance, Demetriades and his colleagues 62 employed ML to infer various parameters on the pharmacological impact of cancer drugs. B¨orlin and his colleagues 95 employed model parameters obtained both from the literature as well as ML-derived ones.…”

Fast post-process Bayesian inference with Sparse Variational Bayesian Monte Carlo

“…At the same time, vbmc computes a fast, tractable approximation of the posterior thanks to variational inference and Bayesian quadrature [O'Hagan, 1991, Ghahramani andRasmussen, 2002b], which yields a lower bound to the model evidence. vbmc works with noisy likelihoods [Acerbi, 2020] and has been applied in fields such as neuroscience [Stine et al, 2020], nuclear engineering [Che et al, 2021], and cancer research [Demetriades et al, 2022].…”