It has been known that TNBC respond to anticancer drugs differently from ER and HER2 positive breast cancers due to distinct disease mechanisms [38].

To reach durable clinical responses, cancer patients who relapse and become refractory to standard chemotherapy need novel multi-targeted drug combinations which can effectively overcome the emergence of drug resistance [2–4]. For example, the synergistic effects have been investigated for certain alkylating agents and purine analogs (cyclophosphamide and fludarabine), platin-and-antimetabolite combinations (cisplatin and fluorouracil; cisplatin and gemcitabine), and dual-antimetabolite combinations (gemcitabine and capecitabine). However, as the focus of this study was to show the validity of using machine learning methods to predict the CSS score, we decided to explore other chemical fingerprint features as a future step. We foresee that such a cross experimental design and its CSS and S scoring methods should allow a scale-up of drug combination testing especially for patient-derived cancer cells. Empowering the cross design with the CSS and S scoring methods, we are foreseeing a lower technical barrier to carry out large-scale drug combination studies with minimal cellular materials. The advantage of CSS is that it allows a direct comparison of the sensitivity between a drug combination and its single drugs, and hence facilitates the quantification of drug synergy. Current cancer chemotherapy treatments typically employ drug combinations in which the dose of each agent is pushed to the brink of unacceptable toxicity. RMSE: root mean square error, R2: coefficient of determination, COR: Pearson correlation, MAE: mean absolute error. Despite the difference in the level of variable importance, all the DNA topoisomerases showed positive coefficients in 38 of 39 cell lines, suggesting that targeting DNA topoisomerases were associated with a higher CSS. The Pearson correlations between CSS1 and CSS2 for the drug combinations that involve a given drug were shown on top of each bar. We considered a drug combination as a combination of their targets and chemical fingerprints. Despite great advances in the understanding of cancer, there remains a major challenge to develop more effective anti-cancer treatments. To evaluate the robustness of the CSS values further, we permuted the O’Neil data randomly and recalculated the correlations between CSS1 and CSS2. With the help of cross design and its data analysis tools, drug combination discovery can be more quickly advanced and may eventually lead to the validation of personalized drug combinations in clinical trials. (12). This is a PLoS Computational Biology Methods paper. The resulting feature set for a single drug included 398 validated and predicted targets and 166 MACCS fingerprints (S1 and S2 Tables). (1)

Examples of the synergistic but not sensitive drug combinations. These results showed that the cross design can be reliably utilized as a cost-effective strategy in a primary screen to detect the most significant synergistic or antagonistic drug combinations. Yes The MACCS fingerprints of the drugs were determined using the SMILES strings with the R package rcdk [26]. Formal analysis, Taken together, we showed that the cross design and its scoring methods allow a more systematic and cost-effective evaluation of drug combinations. Cancer is one of the main causes of death worldwide. At the first stage, single-drug screening was done using 8 concentrations to determine the IC50 concentration for each drug with six replicates. Once the ground truth had been determined using the full dose-response matrix data, we then asked the question: Can the S synergy scores that were determined using the cross design correctly identify the most significant synergistic and antagonistic drug combination hits that were confirmed using the full matrix design? Taken together, CSS1 and CSS2 values are highly consistent and therefore supported their averaging as a summary for the drug combination sensitivity score. For drug-target profiles we collected known targets that were experimentally validated as well as the additional secondary targets that were predicted with high confidence using the SEA method. However, we could not find significant improvement on the prediction accuracy when including the SEA-predicted secondary targets (Table 2), suggesting that the unknown targets for a compound may contribute minimally to the drug combination sensitivity. The use of Chou-Talalay’s CI method in quantifying synergism or antagonism is increasing steadily during the past two decades, however, confusing questions and pitfalls were still frequently raised by insufficient understanding of the theory, especially reflected when researchers trying to use the computerized software to design and conduct experiments. Repaglinide and glyburide belong to two different subtypes of insulin secretagogues, and these subtypes differ in molecular structure, profile of action, and excretion mechanism. The aim was then to use the S synergy score which was determined by the cross design data to predict the ground truth determined by the full matrix design. Therefore, the synergy score can be interpreted as the extra percentage inhibition effect beyond the expectation. https://doi.org/10.1371/journal.pcbi.1006752.t003.

On the other hand, the S synergy score and the CSS drug combination sensitivity score utilize the same unit as the percentage of inhibition of cell viability.

treatments, at higher doses and at later stages of treatment. The patent at issue in Novo Nordisk was directed at the treatment of noninsulin-dependent diabetes mellitus using a novel combination of repaglinide (marketed as Prandinlin in the United States) and metformin (sold under the trade name Glucophage). Am. The degree of synergy is often calculated as the deviation of the observed drug combination effect from the reference, which is defined as the expectation effect if the drugs are not interacting. The Federal Circuit’s decision in Novo Nordisk A/S v. Caraco Pharmaceutical Labs. In all, only 78 nude mice (10 averaged data points) have been used. (8) Our experimental-computational approach could be utilized as an efficient pipeline for improving the discovery rate in high-throughput drug combination screening, particularly for primary patient samples which are difficult to obtain. Learn how your comment data is processed. To assess the degree of synergy from the cross design, we developed an S synergy score based on the difference between the observed CSS score and the baseline effect predicted by a reference model.



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