SYNDEEP: a deep learning approach for the prediction of cancer drugs synergy.

Drug combinations can be the prime strategy for increasing the initial treatment options in cancer therapy. However, identifying the combinations through experimental approaches is very laborious and costly. Notably, in vitro and/or in vivo examination of all the possible combinations might not be plausible. This study presented a novel computational approach to predicting synergistic drug combinations. Specifically, the deep neural network-based binary classification was utilized to develop the model. Various physicochemical, genomic, protein-protein interaction and protein-metabolite interaction information were used to predict the synergy effects of the combinations of different drugs. The performance of the constructed model was compared with shallow neural network (SNN), k-nearest neighbors (KNN), random forest (RF), support vector machines (SVMs), and gradient boosting classifiers (GBC). Based on our findings, the proposed deep neural network model was found to be capable of predicting synergistic drug combinations with high accuracy. The prediction accuracy and AUC metrics for this model were 92.21% and 97.32% in tenfold cross-validation. According to the results, the integration of different types of physicochemical and genomics features leads to more accurate prediction of synergy in cancer drugs

Transformative dynamism in pharmaceutical and biomedical research: Complexity of integration of innovative R & D hubs

Introduction: To be fully functional, pharmaceutical, and biomedical research centers need to be transformed to become innovative research and development (R & D) hubs. Such transformation, however, is a dynamic complex matter. Methods: To establish an innovative R & D hub, a simple and concise manifesto is conceptualized for the nonlinear dynamic transformation towards an innovative research hub to reinforce the transition of the 2nd generation R & D centers. Results: Interdisciplinary research is the most demanded field of research to overcome various multi-sided health issues. To become an innovative R & D hub, pharmaceutical centers must function as a small-scale physical infrastructure to support the inter-communication of scientists and provide specific technological needs to promote the related innovation and entrepreneurship with advanced business plans and prototypes. Given that a success paradigm within an unorderly surrounding setting has already been condemned to fail, the orderly integration of nested systems and groups should be carefully implemented towards a shared vision with formal and tacit agreements among all parties, including academia, industry, and finance team. Conclusion: To achieve a fully functional innovative R & D hub, a "know-how" approach with the systems thinking mindset within all the parties is of paramount necessity. The healthier the order of the whole working system is, the more effective will be the encompassed entitles and players. However, systems should have several checkpoints to enhance clarity and evade discrepancy and divergence. Since the medication is a highly trusted and needed public enterprise, the drug discovery and development paradigm should be practiced at the highest speed with maximum transparency and accountability

Renewed interests in carbonic anhydrase IX in relevance to breast cancer treatment

The highly proliferating cancerous cells can form permissive accommodating milieu – the so-called tumor microenvironment (TME). During the initiation of solid tumors, hypoxia plays a key role in glycolysis, which can trigger the anomalous overexpression of several enzymes and transporters involved in the metabolism of glucose. Of these, carbonic anhydrases (CAs), especially CAIX, together with other molecular machinery involved in the production/trafficking of acidic byproducts, play key roles in the regulation of intracellular and extracellular pH. CAIX, along with other molecular machinery of cancer cells such as Na+/H+ exchanger 1 (NHE1) and V-type H+-ATPase (V-ATPase), alkalinizes the tumor cells and maintains the acidic pH condition within the extracellular fluid of the TME. It facilitates the progression and metastasis of cancer and intensifies the migration and invasion of cancer cells. Thus, inhibition of CAIX can be considered a highly effective and promising therapeutic strategy in the treatment of aggressive tumors

Achievements and beyond: Scientific trajectory of Professor Mohammad A. Rafi

This biography highlights the scientific trajectory of Professor Mohammad A. Rafi, Ph.D., who, in particular, has greatly advanced the field of neurodegenerative disorders during his long and successful tenure at Jefferson Medical College, Thomas Jefferson University. This Editorial recognizes, above all, Professor Rafi's significant contributions to the study of lysosomal storage disorders as they relate to Krabbe Disease

A voting-based machine learning approach for classifying biological and clinical datasets.

BACKGROUND: Different machine learning techniques have been proposed to classify a wide range of biological/clinical data. Given the practicability of these approaches accordingly, various software packages have been also designed and developed. However, the existing methods suffer from several limitations such as overfitting on a specific dataset, ignoring the feature selection concept in the preprocessing step, and losing their performance on large-size datasets. To tackle the mentioned restrictions, in this study, we introduced a machine learning framework consisting of two main steps. First, our previously suggested optimization algorithm (Trader) was extended to select a near-optimal subset of features/genes. Second, a voting-based framework was proposed to classify the biological/clinical data with high accuracy. To evaluate the efficiency of the proposed method, it was applied to 13 biological/clinical datasets, and the outcomes were comprehensively compared with the prior methods. RESULTS: The results demonstrated that the Trader algorithm could select a near-optimal subset of features with a significant level of p-value \u3c 0.01 relative to the compared algorithms. Additionally, on the large-sie datasets, the proposed machine learning framework improved prior studies by ~ 10% in terms of the mean values associated with fivefold cross-validation of accuracy, precision, recall, specificity, and F-measure. CONCLUSION: Based on the obtained results, it can be concluded that a proper configuration of efficient algorithms and methods can increase the prediction power of machine learning approaches and help researchers in designing practical diagnosis health care systems and offering effective treatment plans

Breast cancer vaccination comes to age: impacts of bioinformatics

Introduction: Breast cancer, as one of the major causes of cancer death among women, is the central focus of this study. The recent advances in the development and application of computational tools and bioinformatics in the field of immunotherapy of malignancies such as breast cancer have emerged the new dominion of immunoinformatics, and therefore, next generation of immunomedicines. Methods: Having reviewed the most recent works on the applications of computational tools, we provide comprehensive insights into the breast cancer incidence and its leading causes as well as immunotherapy approaches and the future trends. Furthermore, we discuss the impacts of bioinformatics on different stages of vaccine design for the breast cancer, which can be used to produce much more efficient vaccines through a rationalized time- and cost-effective in silico approaches prior to conducting costly experiments. Results: The tools can be significantly used for designing the immune system-modulating drugs and vaccines based on in silico approaches prior to in vitro and in vivo experimental evaluations. Application of immunoinformatics in the cancer immunotherapy has shown its success in the pre-clinical models. This success returns back to the impacts of several powerful computational approaches developed during the last decade. Conclusion: Despite the invention of a number of vaccines for the cancer immunotherapy, more computational and clinical trials are required to design much more efficient vaccines against various malignancies, including breast cancer