Lezon, T. R. | Search bioRxiv

bioRxiv2021

Predicting the effects of drug combinations using probabilistic matrix factorization

Drug development is costly and time-consuming, and developing novel practical strategies for creating more effective treatments is imperative. One possible solution is to prescribe drugs in combination. Synergistic drug combinations could allow lower doses of each constituent drug, reducing adverse reactions and drug resistance. However, it is not feasible to sufficiently test every combination of drugs for a given illness to determine promising synergistic combinations. Since there is a finite amount of time and resources available for finding synergistic combinations, a model that can identify synergistic combinations from a limited subset of all available combinations could accelerate development of therapeutics. By applying recommender algorithms, such as the low-rank matrix completion algorithm Probabilistic Matrix Factorization (PMF), it may be possible to identify synergistic combinations from partial information of the drug interactions. Here, we use PMF to predict the efficacy of two-drug combinations using the NCI ALMANAC, a robust collection of pairwise drug combinations of 104 FDA-approved anticancer drugs against 60 common cancer cell lines. We find that PMF is able predict drug combination efficacy with high accuracy from a limited set of combinations and is robust to changes in the individual training data. Moreover, we propose a new PMF-guided experimental design to detect all synergistic combinations without testing every combination.

systems biology↗

bioRxiv2020

Computational response modeling reveals context dependent Akt activity in luminal breast cancer cells

Aberrant signaling through insulin (Ins) and insulin-like growth factor I (IGF1) receptors contributes to the risk and advancement of many cancer types by activating cell survival cascades. Mechanistic computational modeling of such pathways provides insights into each components role in the cell response. In previous computational models, the two receptors were treated as indistinguishable, missing the opportunity to delineate their distinct roles in cancer progression. Here, a dual receptor (IGF1R & InsR) computational model elucidated new experimental hypotheses on how differential early responses emerge. Complementary to our previous findings, the model suggested that the regulation of insulin receptor substrate (IRS) is critical in inducing differential MAPK and Akt activation. As predicted, perturbing ribosomal protein S6 kinase (RPS6K) kinase activity led to an increased Akt activation with insulin stimulation compared to IGF1 stimulation. Being able to discern differential downstream signaling, we can explore improved anti-IGF1R cancer therapies by eliminating emergence of compensation mechanisms, without disrupting InsR signaling. ImplicationsThe study shows, both experimentally and through computational models, that IGF1 and insulin receptor signaling pathways respond differently to RPS6K inhibition.

systems biology↗