Development of an analytical-predictive model based on the integration of artificial intelligence and transcriptomic data applied to the identification of molecular targets of (bio)pharmaceutical interest

• Grant number: 24/07829-5
• Support Opportunities: Research Grants - Innovative Research in Small Business - PIPE
• Start date: October 01, 2024
• End date: September 30, 2026
• Field of knowledge: Interdisciplinary Subjects
• Principal Investigator: Rodrigo Pinheiro Araldi
• Grantee: Rodrigo Pinheiro Araldi
• Company: Biodecision Analytics Ltda
• CNAE: Consultoria em tecnologia da informação ; Tratamento de dados, provedores de serviços de aplicação e serviços de hospedagem na internet ; Pesquisa e desenvolvimento experimental em ciências físicas e naturais
• City: São Paulo
• Pesquisadores principais: João Rafael Dias Pinto
• Associated researchers: Benedito Faustinoni Neto ; Danilo Dias da Silva
• Associated research grant: 23/06116-2 - Development of an analytical-predictive model based on the integration of artificial intelligence and transcriptomic data applied to the identification of molecular targets of (bio)pharmaceutical interest, AP.PIPE
• Associated scholarship(s): 24/17577-3 - Development of an analytical-predictive model based on the integration of artificial intelligence and transcriptomic data applied to the identification of molecular targets of (bio)pharmaceutical interest, BP.PIPE

Abstract

Drug research and development (R&D) is an essential activity in the (bio)pharmaceutical sector. To meet the constant demands of the sector, high-throughput screening techniques such as RNA sequencing (RNA-Seq) have been widely used to accelerate drug R&D and reduce costs related to this systematic process. This is because RNA-Seq allows you to identify therapeutic targets, predict the mechanism of action and even predict the possible adverse effects of investigational products. However, by generating large volumes of data (Big Data) that require multi- and interdisciplinary knowledge in molecular biology, bioinformatics, Data Analytics, statistics and artificial intelligence, in addition to requiring advanced knowledge in programming, the results obtained by RNA-Seq remain being underused or used inappropriately, leading to false-positive results that negatively impact drug R&D. In this sense, BioDecision developed an innovative and disruptive analytical-predictive model (BDASeq), which brings together all the tools necessary to accurately analyze RNA-Seq data. As it runs on Google Cloud Platform (GCP), this model also offers secure data storage and has a cloud computing service. BDASeq was validated using real RNA-Seq data (available in the SRA public repository) from patients with neurodegenerative Huntington's disease, making this the largest transcriptomic study ever carried out on the disease. The results obtained made it possible to identify differentially expressed genes that can serve as therapeutic targets with greater accuracy than the traditionally used approach. These targets bring hope to patients, as the products encoded by these genes can be pharmacologically modified, offering opportunities for the development of new drugs. For these reasons, the BDASeq model was awarded at the largest world congress on rare diseases (MENA Rare Disease Congress, United Arab Emirates, 2024). However, the BDASeq model still requires programming knowledge. Therefore, BioDecision aims to transform the model into the BDASeq WebService. This development (proposed for the PIPE Phase 2 project) aims to offer the accuracy of BDASeq, combined with scalable high-performance processing capacity and secure data storage in line with the General Data Protection Law (LGPD) and the Health Insurance Portability and Accountability Act (HIPAA), offered by GCP, but with a user-friendly graphical interface. Together, these characteristics make it possible to meet all the needs not yet met by the transcriptomic data analysis segment, which represents the segment with the highest annual growth rate within the drug development market. (AU)