Combating Viral Diseases in the Era of Systems Medicine.

Viruses can cause many diseases resulting in disabilities and death. Fortunately, advances in systems medicine enable the development of effective therapies for treating viral diseases, of vaccines to prevent viral infections, as well as of diagnostic tools to mitigate the risk and reduce the death toll. Characterizing the SARS-CoV-2 gene sequence and the role of its spike protein in infection informs development of small molecule drugs, antibodies, and vaccines to combat infection and complication, as well as to end the pandemic. Drug repurposing of small molecule drugs is a viable strategy to combat viral diseases; the key concepts include (1) linking a drug candidate's pharmacological network to its pharmacodynamic response in patients; (2) linking a drug candidate's physicochemical properties to its pharmacokinetic characteristics; and (3) optimizing the safe and effective dosing regimen within its therapeutic window. Computational integration of drug-induced signaling pathways with clinical outcomes is useful to inform selection of potential drug candidates with respect to safety and effectiveness. Key pharmacokinetic and pharmacodynamic principles for computational optimization of drug development include a drug candidate's Cminss/IC95 ratio, pharmacokinetic characteristics, and systemic exposure-response relationship, where Cminss is the trough concentration following multiple dosing. In summary, systems medicine approaches play a vital role in global success in combating viral diseases, including global real-time information sharing, development of test kits, drug repurposing, discovery and development of safe, effective therapies, detection of highly transmissible and deadly variants, and development of vaccines.

A Computational Platform and Guide for Acceleration of Novel Medicines and Personalized Medicine.

In the era of big data and informatics, computational integration of data across the hierarchical structures of human biology enables discovery of new druggable targets of disease and new mode of action of a drug. We present herein a computational framework and guide of integrating drug targets, gene expression data, transcription factors, and prior knowledge of protein interactions to computationally construct the signaling network (mode of action) of a drug. In a similar manner, a disease network is constructed using its disease targets. And then, drug candidates are computationally prioritized by computationally ranking the closeness between a disease network and a drug's signaling network. Furthermore, we describe the use of the most perturbed HLA genes to assess the safety risk for immune-mediated adverse reactions such as Stevens-Johnson syndrome/toxic epidermal necrolysis.

Computational Approaches to Accelerating Novel Medicine and Better Patient Care from Bedside to Benchtop.

Some successes have been achieved in the war on cancer over the past 30 years with recent efforts on protein kinase inhibitors. Nonetheless, we are still facing challenges due to cancer evolution. Cancers are complex and heterogeneous due to primary and secondary mutations, with phenotypic and molecular heterogeneity manifested among patients of a cancer, and within an individual patient throughout the disease course. Our understanding of cancer genomes has been facilitated by advances in omics and in bioinformatics technologies; major areas in cancer research are advancing in parallel on many fronts. Computational methods have been developed to decipher the molecular complexity of cancer and to identify driver mutations in cancers. Utilizing the identified driver mutations to develop effective therapy would require biological linkages from cellular context to clinical implication; for this purpose, computational mining of biomedical literature facilitates utilization of a huge volume of biomedical research data and knowledge. In addition, frontier technologies, such as genome editing technologies, are facilitating investigation of cancer mutations, and opening the door for developing novel treatments to treat diseases. We will review and highlight the challenges of treating cancers, which behave like moving targets due to mutation and evolution, and the current state-of-the-art research in the areas mentioned above.