Informatics Approaches for Predicting, Understanding, and Testing Cancer Drug Combinations

CFSSynergy: Combining Feature-Based and Similarity-Based Methods for Drug Synergy Prediction

Drug synergy prediction plays a vital role in cancer treatment. Because experimental approaches are labor-intensive and expensive, computational-based approaches get more attention. There are two types of computational methods for drug synergy prediction: feature-based and similarity-based. In feature-based methods, the main focus is to extract more discriminative features from drug pairs and cell lines to pass to the task predictor. In similarity-based methods, the similarities among all drugs and cell lines are utilized as features and fed into the task predictor. In this work, a novel approach, called CFSSynergy, that combines these two viewpoints is proposed. First, a discriminative representation is extracted for paired drugs and cell lines as input. We have utilized transformer-based architecture for drugs. For cell lines, we have created a similarity matrix between proteins using the Node2Vec algorithm. Then, the new cell line representation is computed by multiplying the protein-protein similarity matrix and the initial cell line representation. Next, we compute the similarity between unique drugs and unique cells using the learned representation for paired drugs and cell lines. Then, we compute a new representation for paired drugs and cell lines based on the similarity-based features and the learned features. Finally, these features are fed to XGBoost as a task predictor. Two well-known data sets were used to evaluate the performance of our proposed method: DrugCombDB and OncologyScreen. The CFSSynergy approach consistently outperformed existing methods in comparative evaluations. This substantiates the efficacy of our approach in capturing complex synergistic interactions between drugs and cell lines, setting it apart from conventional similarity-based or feature-based methods.

Pharmacological approaches to understanding protein kinase signaling networks

Protein kinases play vital roles in controlling cell behavior, and an array of kinase inhibitors are used successfully for treatment of disease. Typical drug development pipelines involve biological studies to validate a protein kinase target, followed by the identification of small molecules that effectively inhibit this target in cells, animal models, and patients. However, it is clear that protein kinases operate within complex signaling networks. These networks increase the resilience of signaling pathways, which can render cells relatively insensitive to inhibition of a single kinase, and provide the potential for pathway rewiring, which can result in resistance to therapy. It is therefore vital to understand the properties of kinase signaling networks in health and disease so that we can design effective multi-targeted drugs or combinations of drugs. Here, we outline how pharmacological and chemo-genetic approaches can contribute to such knowledge, despite the known low selectivity of many kinase inhibitors. We discuss how detailed profiling of target engagement by kinase inhibitors can underpin these studies; how chemical probes can be used to uncover kinase-substrate relationships, and how these tools can be used to gain insight into the configuration and function of kinase signaling networks.

Tumor-specific radiosensitizing effect of the ATM inhibitor AZD0156 in melanoma cells with low toxicity to healthy fibroblasts

PURPOSE

Despite new treatment options, melanoma continues to have an unfavorable prognosis. DNA damage response (DDR) inhibitors are a promising drug class, especially in combination with chemotherapy (CT) or radiotherapy (RT). Manipulating DNA damage repair during RT is an opportunity to exploit the genomic instability of cancer cells and may lead to radiosensitizing effects in tumors that could improve cancer therapy.

METHODS

A panel of melanoma-derived cell lines of different origin were used to investigate toxicity-related clonogenic survival, cell death, and cell cycle distribution after treatment with a kinase inhibitor (KI) against ATM (AZD0156) or ATR (VE-822, berzosertib), irradiation with 2 Gy, or a combination of KI plus ionizing radiation (IR). Two fibroblast cell lines generated from healthy skin tissue were used as controls.

RESULTS

Clonogenic survival indicated a clear radiosensitizing effect of the ATM inhibitor (ATMi) AZD0156 in all melanoma cells in a synergistic manner, but not in healthy tissue fibroblasts. In contrast, the ATR inhibitor (ATRi) VE-822 led to additive enhancement of IR-related toxicity in most of the melanoma cells. Both inhibitors mainly increased cell death induction in combination with IR. In healthy fibroblasts, VE-822 plus IR led to higher cell death rates compared to AZD0156. A significant G2/M block was particularly induced in cancer cells when combining AZD0156 with IR.

CONCLUSION

ATMi, in contrast to ATRi, resulted in synergistic radiosensitization regarding colony formation in melanoma cancer cells, while healthy tissue fibroblasts were merely affected with respect to cell death induction. In connection with an increased number of melanoma cells in the G2/M phase after ATMi plus IR treatment, ATMi seems to be superior to ATRi in melanoma cancer cell treatments when combined with RT.

DeepTraSynergy: drug combinations using multimodal deep learning with transformers

MOTIVATION

Screening bioactive compounds in cancer cell lines receive more attention. Multidisciplinary drugs or drug combinations have a more effective role in treatments and selectively inhibit the growth of cancer cells.

RESULTS

Hence, we propose a new deep learning-based approach for drug combination synergy prediction called DeepTraSynergy. Our proposed approach utilizes multimodal input including drug-target interaction, protein-protein interaction, and cell-target interaction to predict drug combination synergy. To learn the feature representation of drugs, we have utilized transformers. It is worth noting that our approach is a multitask approach that predicts three outputs including the drug-target interaction, its toxic effect, and drug combination synergy. In our approach, drug combination synergy is the main task and the two other ones are the auxiliary tasks that help the approach to learn a better model. In the proposed approach three loss functions are defined: synergy loss, toxic loss, and drug-protein interaction loss. The last two loss functions are designed as auxiliary losses to help learn a better solution. DeepTraSynergy outperforms the classic and state-of-the-art models in predicting synergistic drug combinations on the two latest drug combination datasets. The DeepTraSynergy algorithm achieves accuracy values of 0.7715 and 0.8052 (an improvement over other approaches) on the DrugCombDB and Oncology-Screen datasets, respectively. Also, we evaluate the contribution of each component of DeepTraSynergy to show its effectiveness in the proposed method. The introduction of the relation between proteins (PPI networks) and drug-protein interaction significantly improves the prediction of synergistic drug combinations.

AVAILABILITY AND IMPLEMENTATION

The source code and data are available at https://github.com/fatemeh-rafiei/DeepTraSynergy.

Broadening the spectrum of ivermectin: Its effect on Trypanosoma cruzi and related trypanosomatids

Chagas disease is an endemic American parasitosis, caused by Trypanosoma cruzi. The current therapies, benznidazole (BZN) and nifurtimox (NFX), show limited efficacy and multiple side effects. Thus, there is a need to develop new trypanocidal strategies. Ivermectin (IVM) is a broad-spectrum antiparasitic drug with low human and veterinary toxicity with effects against T. brucei and Leishmania spp. Considering this and its relatively low cost, we evaluate IVM as a potential repurposed trypanocidal drug on T. cruzi and other trypanosomatids. We found that IVM affected, in a dose-dependent manner, the proliferation of T. cruzi epimastigotes as well as the amastigotes and trypomastigotes survival. The Selectivity Index for the amastigote stage with respect to Vero cells was 12. The IVM effect was also observed in Phytomonas jma 066 and Leishmania mexicana proliferation but not in Crithidia fasciculata. On the epimastigote stage, the IVM effect was trypanostatic at 50 μM but trypanocidal at 100 μM. The assays of the drug combinations of IVM with BNZ or NFX showed mainly additive effects among combinations. In silico studies showed that classical structures belonging to glutamate-gated Cl channels, the most common IVM target, are absent in kinetoplastids. However, we found in the studied trypanosomatid genomes one copy for putative IMPα and IMPβ, potential targets for IVM. The putative IMPα genes (with 76% similarity) showed conserved Armadillo domains but lacked the canonical IMPβ binding sequence. These results allowed us to propose a novel molecular target in T. cruzi and suggest IVM as a good candidate for drug repurposing in the Chagas disease context.

Comparative analysis of molecular fingerprints in prediction of drug combination effects

Application of machine and deep learning methods in drug discovery and cancer research has gained a considerable amount of attention in the past years. As the field grows, it becomes crucial to systematically evaluate the performance of novel computational solutions in relation to established techniques. To this end, we compare rule-based and data-driven molecular representations in prediction of drug combination sensitivity and drug synergy scores using standardized results of 14 high-throughput screening studies, comprising 64 200 unique combinations of 4153 molecules tested in 112 cancer cell lines. We evaluate the clustering performance of molecular representations and quantify their similarity by adapting the Centered Kernel Alignment metric. Our work demonstrates that to identify an optimal molecular representation type, it is necessary to supplement quantitative benchmark results with qualitative considerations, such as model interpretability and robustness, which may vary between and throughout preclinical drug development projects.

DrugComb: an integrative cancer drug combination data portal

Drug combination therapy has the potential to enhance efficacy, reduce dose-dependent toxicity and prevent the emergence of drug resistance. However, discovery of synergistic and effective drug combinations has been a laborious and often serendipitous process. In recent years, identification of combination therapies has been accelerated due to the advances in high-throughput drug screening, but informatics approaches for systems-level data management and analysis are needed. To contribute toward this goal, we created an open-access data portal called DrugComb (https://drugcomb.fimm.fi) where the results of drug combination screening studies are accumulated, standardized and harmonized. Through the data portal, we provided a web server to analyze and visualize users’ own drug combination screening data. The users can also effectively participate a crowdsourcing data curation effect by depositing their data at DrugComb. To initiate the data repository, we collected 437 932 drug combinations tested on a variety of cancer cell lines. We showed that linear regression approaches, when considering chemical fingerprints as predictors, have the potential to achieve high accuracy of predicting the sensitivity of drug combinations. All the data and informatics tools are freely available in DrugComb to enable a more efficient utilization of data resources for future drug combination discovery.

Charting the Fragmented Landscape of Drug Synergy

Even as the clinical impact of drug combinations continues to accelerate, no consensus on how to quantify drug synergy has emerged. Rather, surveying the landscape of drug synergy reveals the persistence of historical fissures regarding the appropriate domains of conflicting synergy models - fissures impacting all aspects of combination therapy discovery and deployment. Herein we chronicle the impact of these divisions on: (i) the design, interpretation, and reproducibility of high-throughput combination screens; (ii) the performance of algorithms to predict synergistic mixtures; and (iii) the search for higher-order synergistic interactions. Further progress in each of these subfields hinges on reaching a consensus regarding the long-standing rifts in the field.

Prediction of drug synergy score using ensemble based differential evolution

Prediction of drug synergy score is an ill-posed problem. It plays an efficient role in the medical field for inhibiting specific cancer agents. An efficient regression-based machine learning technique has an ability to minimise the drug synergy prediction errors. Therefore, in this study, an efficient machine learning technique for drug synergy prediction technique is designed by using ensemble based differential evolution (DE) for optimising the support vector machine (SVM). Because the tuning of the attributes of SVM kernel regulates the prediction precision. The ensemble based DE employs two trial vector generation techniques and two control attributes settings. The initial generation technique has the best solution and the other is without the best solution. The proposed and existing competitive machine learning techniques are applied to drug synergy data. The extensive analysis demonstrates that the proposed technique outperforms others in terms of accuracy, root mean square error and coefficient of correlation.