Drug development in the era of precision medicine

Abstract

For the past three decades, the use of genomics to inform drug discovery and development pipelines has generated both excitement and scepticism. Although earlier efforts successfully identified some new drug targets, the overall clinical efficacy of developed drugs has remained unimpressive, owing in large part to the heterogeneous causes of disease. Recent technological and analytical advances in genomics, however, have now made it possible to rapidly identify and interpret the genetic variation underlying a single patient’s disease, thereby providing a window into patient-specific mechanisms that cause or contribute to disease, which could ultimately enable the ‘precise’ targeting of these mechanisms. Here, we first examine and highlight the successes and limitations of the earlier phases of genomics in drug discovery and development. We then review the current major efforts in precision medicine and discuss the potential broader utility of mechanistically guided treatments going forward.

A Survey and Systematic Assessment of Computational Methods for Drug Response Prediction

“…GENet-Lap and GENet-NLap were the two versions developed from ENet through incorporating the (normalized) Laplacian matrix of a protein-protein interaction network into the last regularization term in Eqn. (1). Interestingly, the two methods outperformed ENet for seven out of the nine metrics ( Figure 1-2, see also Figure S1-S2).…”

“…In this method, B n×n = I n×n in Eqn. (1). We assessed two versions of this model: GENet-Lap and GENet-NLap in which M is set to the Laplacian matrix and the normalized Laplacian matrix of a gene interaction network [14], respectively.…”

“…This approach uses an iterative procedure to set (b j ) and compute the coefficient vector w in Eqn. (1). Here, the drug response data R n×1 = (r i ) n i=1 is assumed to follow a normal distribution N (µ, σ 2 ) with the parameters being estimated as [12]:…”

“…This problem can be solved as easily as solving Eqn. (1). The reason is that one only needs to know how to compute the inner product f (u) · f (v) of image vectors to solve the optimization problem.…”

“…Recent cancer genome studies have suggested that each cancer patient owns a unique profile of gene mutations and that there is no silver bullet for defeating all the cancers. Therefore, how to use the genomic profiles of cancer patients to design a personalized treatment is vital for effective control of disease progression [1,2]. This requires accurate prediction of the patients' response to target-specific drugs and therapies.…”