Zia ur Rehman is a senior researcher in Tero Aittokallio's group at the Institute for Molecular Medicine Finland FIMM, University of Helsinki. In June, he was awarded prestigious 4-year project funding by the Academy of Finland. His project, "Development of methods and tools supporting drug repurposing", started in September 2022.
In this project, Zia ur Rehman and his soon-to-be recruited team will develop several new computational tools and methods supporting drug repurposing for cancer and other indications.
Dr. Rehman has done his PhD in Machine learning in 2013, graduating from the Pakistan Institute of Engineering and Applied Sciences as one of the youngest PhDs in his home country at the age of 27. He has more than seven years of postdoctoral experience in computational drug repurposing and substantial experience in developing online platforms to support the wide drug repurposing research community. Among these popular tools are DrugTargetCommons, DrugTargetProfiler, MICHA and DrugRepo.
His new Academy project has five work packages (WP), four of which are computational while the fifth focuses on experimental validation work. The WPs are summarized in the image below and described in detail at the end of this article.
“By the end of this project, we have hopefully developed several new methods and tools for drug repurposing that will further boost drug discovery applications. By combining the power of text mining and crowdsourcing, we will have a more complete collection of drug-target interactions for FDA-approved drugs”, Dr. Rehman envisions.
Zia ur Rehman is planning to hire three researchers to contribute to the project. Furthermore, the Computational Systems Medicine group led by Tero Aittokallio has several computational and wet lab scientists who will facilitate several work packages (WPs). In addition, Jing Tang and Jehad Aldahdooh from the Network Pharmacology for Precision Medicine group will assist in text mining modules (WP3) and Markus Vähä-Koskela from FIMM is contributing to the experimental validation of top repurposing leads.
“Collaboration is the key to success. Both my local connections and my international collaboration networks will help me to execute this research project effectively”, Dr. Rehman concludes.
Work package 1:
Biochemical and omics data for biological and chemical entities are spread across hundreds of databases. However, the harmonization of data within these databases is still challenging. Therefore, WP1 will focus on developing new software packages and tools for collecting diverse annotations for chemical and biological entities, including drug-target interactions, omics profiles, biological pathways, side effects, compound structures, drug combinations, gene associations, drug indications and clinical status.
Work package 2:
WP2 will produce methods/tools to associate new indications for FDA-approved drugs. Drug repurposing methods in WP2 will be based on multi-modal data and information harmonized in WP1. Zia ur Rehman has already published two review articles (one in the Expert Opinion on Drug Discovery and the other in the Briefings in Bioinformatics) on drug repurposing, where he surveyed the pros and cons of hundreds of computational tools and methods. These past works will help to design new drug repurposing tools.
Work packages 3 and 4:
Drug-target interactions (DTIs) are the basis for target-based drug repurposing. However, drug-target databases lack sufficient data and harmonization, resulting in incomplete target profiling information. There are currently >1500 FDA-approved drugs and nearly 1000 druggable protein targets. However, the average number of protein targets per approved drug is seven. Similarly, there are >31M articles on PubMed; however, only a tiny fraction (0.3%) of these articles has been manually curated in publicly available databases. Though data curation cannot be fully automated, with the improvement in text mining-based pre-trained methods (such as BERT), it is now possible to extract new DTIs. WP3 will produce text mining methods to extract new DTIs. Since text mining is prone to errors, WP4 will combine the power of text mining, machine learning and crowdsourcing to define a robust scoring system for target interactions of FDA-approved drugs.
Work package 5:
Since one cannot fully rely on computational approaches, WP5 will validate the computational methods with the help of the Functional Precision Medicine in Cancer platform at FIMM.