These are interesting times to be in the drug discovery field. The blockbuster business model is falling by the wayside, driven in large part by rapid advances in our understanding of the myriad of molecular drivers of cancer and other complex diseases, rising efficacy bars for new targeted or personalized therapies, and the adoption of value-based (sometimes zero) reimbursement practices for marginally effective drugs or treatment regimens that continue to gain approval.
The increasing move toward addressing rational molecular targets that directly drive disease biology and biomarker-driven clinical practices that enable the tailoring of novel drugs to only the patients that harbor the given molecular target is of benefit to patients and society as both maximally effective outcomes, and management of future healthcare costs will be ensured.
This trend, however, also places extreme pressure on pharmaceutical companies to reduce their timelines, attrition rates, and costs in order to find many more targeted drugs for smaller but more responsive patient populations. There is also an imperative for cancer biologists and the biotech sector to decipher the full genetic complexity and heterogeneity of cancer. This will define next-generation targets for drugging and clinical scenarios in which they have the best shot for gaining approval.
The genomic landscape of cancer (and many other diseases) will represent ground-zero data for the majority of future drug discovery efforts. These are the clearest indicator of what is potentially important to a tumor; and with DNA sequencing no longer representing a bottleneck, the full range of genetic mutations, their frequency within various tumor types, and their staging information will soon be known.
This is being driven by the coordinated efforts of multiple global genome centers, which are making all data publically available. These efforts will in the future deliver more complex and useful datasets, such as correlating which cancer genes commonly occur together (so that candidate drug response–defining biomarkers and potential drug combination strategies can be defined) or which mutations represent early events (to identify targets with less intratumoral heterogeneity). Recapitulating this sequencing and bioinformatics infrastructure within any pharma company will be pointless; however, deciphering which cancer genes represent good drug targets will be essential.
The first step in determining which mutant genes represent good drug targets requires the definition of which genes are actually true disease drivers, rather than just random noise in genetically unstable tumors. If a gene is mutated frequently, then it is usually safe to assume that it is, or was, a driver (confirmed through validation experiments). However, most candidate cancer genes are not very common, requiring a systematic functional genomics effort to assess exactly which elicit a significant tumorigenic effect.
This would involve implementing a large, coordinated study of all the hallmarks of cancer (as not all genes will increase cell proliferation) and a new generation of genome-editing technologies to directly alter the sequence of endogenous genes in human cells growing in cell culture.
A technique pioneered by Horizon Discovery, GENESIS™, allows the full range of genetic alterations to be made in human cells (small or large gene deletions, point-mutations, reversion of mutations to wild type, translocations, amplifications, and transgene insertions), with unparalleled precision (Figure 1). The technique harnesses the power of recombinant adeno-associated viruses to activate homologous recombination, a natural high-fidelity DNA repair mechanism.