Searching for synergies: matrix algebraic approaches for efficient pair screening

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.

Human umbilical cord perivascular cells maintain regenerative traits following exposure to cyclophosphamide

Chemotherapies can cause germ cell depletion and gonadal failure. When injected post-chemotherapy, mesenchymal stromal cells (MSCs) from various sources have been shown to have regenerative effects in rodent models of chemotherapy-induced gonadal injury. Here, we evaluated two properties of a novel source of MSC, first trimester (FTM) human umbilical cord perivascular cells (HUCPVCs) (with increased regenerative potential compared to older sources), that may render them a promising candidate for chemotherapeutic gonadal injury prevention. Firstly, their ability to resist the cytotoxic effects of cyclophosphamide (CTX) in vitro, as compared to term HUCPVCs and bone marrow cells (BMSCs); and secondly, whether they prevent gonadal dysfunction if delivered prior to gonadotoxic therapy in vivo. BMSC, FTM HUCPVC, term HUCPVC, and control NTERA2 cells were treated with moderate (150 μmol/L) and high (300 μmol/L) doses of CTX in vitro. Viability, proliferative capacity, mesenchymal cell lineage markers and differentiation capacity, immunogenicity, and paracrine gene expression were assessed. CTX was administered to Wistar rats 2 days following an intra-ovarian injection of FTM HUCPVC. HUCPVC survival and ovarian follicle numbers were assessed using histological methods. We conclude that FTM HUCPVC maintain key regenerative properties following chemotherapy exposure and that pre-treatment with these cells may prevent CTX-induced ovarian damage in vivo. Therefore, HUCPVCs are promising candidates for fertility preservation.

Modelling of compound combination effects and applications to efficacy and toxicity: state-of-the-art, challenges and perspectives

The development of treatments involving combinations of drugs is a promising approach towards combating complex or multifactorial disorders. However, the large number of compound combinations that can be generated, even from small compound collections, means that exhaustive experimental testing is infeasible. The ability to predict the behaviour of compound combinations in biological systems, whittling down the number of combinations to be tested, is therefore crucial. Here, we review the current state-of-the-art in the field of compound combination modelling, with the aim to support the development of approaches that, as we hope, will finally lead to an integration of chemical with systems-level biological information for predicting the effect of chemical mixtures.