Bulletin No. 2, 2019

13 They Stand on the Shoulders of Giants Drug discovery requires multidisciplinary effort. To unravel the genetic bases of complex diseases, Prof. So Hon-cheong , a statistical geneticist and computational biologist, has been tapping into the rapid growth of genotyping technologies and genome-wide association studies (GWAS). ‘There are many challenges and mysteries to solve in the field. I’ve benefitted from the free and collaborative atmosphere at CUHK,’ he says. Psychiatric disorders inflict a significant burden on health globally, but the current treatment strategies are far from perfect. Professor So’s team focuses on drug ‘repositioning’ or using existing drugs for new indications, as ‘re-using’ existing drugs can be more cost-effective and time-saving. They have identified drug candidates based on GWAS results and applied the method to various psychiatric disorders. While many animal or cell-based models are available to decipher the molecular basis of diseases, human genomics data are still indispensable as many disease models are still inadequate. He says, ‘Psychiatric disorders are very difficult to be modeled in animals, but GWAS data provide a unique opportunity to unravel the genetic basis of such disorders.’ A computational approach has been developed to find promising drugs for new indications. They estimated gene expression changes from GWAS data and compared those against the expression profiles of different drugs. Those drugs with an opposite expression pattern are considered as candidates. For example, non-steroidal anti-inflammatory agents like aspirin and a cyclooxygenase-2 (COX-2) inhibitor are prioritized as candidates for bipolar disorder and schizophrenia. Main challenges include conceiving ways to map GWAS results to genes, such that they can be ‘matched’ to drug expression data. The team verified their method by investigating whether they can rediscover some known drugs or those included in clinical trials. The verification process isn’t easy. A large number of candidates can be found, but not every candidate can be experimentally validated. Professor So’s team is also developing other methods like machine learning approaches to complement the methodology. They are collaborating with university hospitals in mainland China and planning to utilize large-scale clinical records to verify some repositioning candidates. ‘Drug discovery has largely been stagnant in the past two decades. Computational drug repositioning helps to provide more medication choices for psychiatric disorders and diseases with few known treatments. Our proposed method may also help find out the efficacy of Chinese medicine and drugs on targets,’ he adds. Drugs Transformer ‘There are many challenges and mysteries to solve in the field. I’ve benefitted from the free and collaborative atmosphere at CUHK.’