New Drug Development and Clinical Trial Design by Applying Genomic Information Management

Abstract

Depending on the patients’ genotype, the same drug may have different efficacies or side effects. With the cost of genomic analysis decreasing and reliability of analysis methods improving, vast amount of genomic information has been made available. Several studies in pharmacology have been based on genomic information to select the optimal drug, determine the dose, predict efficacy, and prevent side effects. This paper reviews the tissue specificity and genomic information of cancer. If the tissue specificity of cancer is low, cancer is induced in various organs based on a single gene mutation. Basket trials can be performed for carcinomas with low tissue specificity, confirming the efficacy of one drug for a single gene mutation in various carcinomas. Conversely, if the tissue specificity of cancer is high, cancer is induced in only one organ based on a single gene mutation. An umbrella trial can be performed for carcinomas with a high tissue specificity. Some drugs are effective for patients with a specific genotype. A companion diagnostic strategy that prescribes a specific drug for patients selected with a specific genotype is also reviewed. Genomic information is used in pharmacometrics to identify the relationship among pharmacokinetics, pharmacodynamics, and biomarkers of disease treatment effects. Utilizing genomic information, sophisticated clinical trials can be designed that will be better suited to the patients of specific genotypes. Genomic information also provides prospects for innovative drug development. Through proper genomic information management, factors relating to drug response and effects can be determined by selecting the appropriate data for analysis and by understanding the structure of the data. Selecting pre-processing and appropriate machine-learning libraries for use as machine-learning input features is also necessary. Professional curation of the output result is also required. Personalized medicine can be realized using a genome-based customized clinical trial design.

Data-Driven Technology Roadmaps to Identify Potential Technology Opportunities for Hyperuricemia Drugs

“…It is helpful to narrow the scope of technology research topics and reduce R&D risks, which not only supports the decision-making of hyperuricemia drug research but also helps to avoid unnecessary R&D costs [14][15][16][17]. Various data analysis tools were explored to analyze potential technology opportunities efficiently [18][19][20], such as bibliometrics [21], citation analysis [22], technology roadmap (TRM) [23], Biterm Topic Model (BTM) [24], Bidirectional Encoder Representation from Transformers (BERT) [25,26], Subject-action-object analysis (SAO), [27] and link prediction [15,28,29]. These tools integrate mathematics, statistics, computer science, and operations research in technology opportunity analysis [16,[30][31][32][33].…”