It is well known that the development of a new drug is exorbitantly expensive and is a process which usually fails. Researchers at PrecisionLife posit that failure and the many patients with unmet treatment needs result from an over simplistic view of disease pathogeneses and an overreliance on a narrow range of target genes and pathways, as well as the idea that patients with the same diagnosis share the same cause of their disease. These conceptual limitations can be overcome by enhanced patient stratification, so that patient subgroups with similar disease etiologies can be identified.
This opens up the possibility of expanding the indication, or repositioning, of many drugs in development or already marketed. Improving the mapping between a proposed mechanism of action of a target or targets and the subgroups of patients who could benefit from acting on that target or targets could overcome issues with current methods of identifying repositioning opportunities in addition to enhancing novel drug discovery.
Combinatorial analysis using advanced analytics and AI can be used to identify combinations of disease-associated factors such as genetic loci, epidemiological and environmental factors that distinguish one patient subgroup from another. The hypothesis-free method discovers disease signatures – disease-associated combinations of 3 to 10 features – associated with variations in disease risk, symptoms, progression rates and therapy response in subgroups of patients using a case-control cohort design. Disease signatures can then be clustered to provide a high-resolution stratification of the patient population, pointing to a subgroup within a heterogeneous patient population which would be most responsive to modulation of a target of interest.
To use this method, the target of candidate or approved drugs was correlated with all of the detailed mechanistic patient stratification insights for 30 disease-stratification studies. This led to the identification of 477 potential indication opportunities across the 30 disease areas where that target or mechanism was found to be strongly associated with one or more clinically relevant patient subgroup in another disease study.
In one example, the gene target for mineralocorticoid receptor (MR) antagonists, NR3C2, was searched across the 30 chronic disease studies to identify clinically relevant patient subgroups which may benefit from MR antagonist treatment. A combinatorial disease signature containing a variant in NR3C2 was identified as highly associated with type 2 diabetes patients who developed at least one of the main complications associated with diabetes, including ketoacidosis, cardiovascular complications, neurological complications and chronic kidney disease. The signature containing the genetic variant was found in 209 cases with type 2 diabetes complications and in no controls, and cases with this signature were significantly more likely to have renal complications. This, supported by evidence in the literature, suggests MR antagonists may be beneficial in patients with type 2 diabetes who are most at risk of developing renal complications.
A second example identified the potential of IL-6R antagonists for patients with amyotrophic lateral sclerosis (ALS) as genetic variants involved in the regulation of IL-6 secretion were part of a combinatorial disease signature significantly associated with a subgroup of ALS patients who were more likely to develop earlier onset and more aggressive forms of the disease. A clinical study supporting this finding involved investigation of tocilizumab in ALS patients, which slowed clinical progression.The combinatorial analysis approach appears to be an accurate and expansive means of finding beneficial new uses for drugs in development, currently marketed, or even withdrawn, helping to overcome the slowness, cost and high failure rate associated with current drug development methods.