Target identification and validation through genetics

Sixty Years of Drug Discovery for Type 2 Diabetes: Where Are We Now?

Today, excluding insulin, there are eight classes of anti-diabetic medicines that have been added to the pharmacy since the introduction of metformin in the mid-1950s; the sulfonylureas, biguanides, thiazolidinediones, α-glucosidase inhibitors, meglitinides, incretins, and sodium glucose transport 2 inhibitors. Does the fact that metformin is still first-line treatment suggest that our drug discovery efforts over the past 60 years have not been good enough? Or does it suggest that diabetes is such a complex disorder that no single treatment, other than gastric bypass surgery, can affect true normalization of not only blood sugar but also the underlying pathologies? Our understanding of the disease has most definitely improved which may bring hope for the future in terms of science, but for it to be beneficial, this science has to be translated into better drug treatments for the disease. In this review, I have examined the eight classes of anti-diabetes drugs from a drug discovery perspective.

Experimental validation of in silico target predictions on synergistic protein targets

Two relatively recent trends have become apparent in current early stage drug discovery settings: firstly, a revival of phenotypic screening strategies and secondly, the increasing acceptance that some drugs work by modulating multiple targets in parallel (‘multi-target drugs’).

Human monogenic disorders — a source of novel drug targets

The decrease in new drug applications and approvals over the past several years results from an underlying crisis in drug target identification and validation. Model organisms are being used to address this problem, in combination with novel approaches such as the International HapMap Project. What has been underappreciated is that discovery of new drug targets can also be revived by traditional Mendelian genetics. A large fraction of the human gene repertoire remains phenotypically uncharacterized, and is likely to encode many unanticipated and novel phenotypes that will be of interest to pharmaceutical and biotechnological drug developers.