Precision Oncology Comes of Age: Designing Best-in-Class Small Molecules by Integrating Two Decades of Advances in Chemistry, Target Biology, and Data Science

Abstract

Small-molecule drugs have enabled the practice of precision oncology for genetically defined patient populations since the first approval of imatinib in 2001. Scientific and technology advances over this 20-year period have driven the evolution of cancer biology, medicinal chemistry, and data science. Collectively, these advances provide tools to more consistently design best-in-class small-molecule drugs against known, previously undruggable, and novel cancer targets. The integration of these tools and their customization in the hands of skilled drug hunters will be necessary to enable the discovery of transformational therapies for patients across a wider spectrum of cancers.

Significance: Target-centric small-molecule drug discovery necessitates the consideration of multiple approaches to identify chemical matter that can be optimized into drug candidates. To do this successfully and consistently, drug hunters require a comprehensive toolbox to avoid following the "law of instrument" or Maslow's hammer concept where only one tool is applied regardless of the requirements of the task. Combining our ever-increasing understanding of cancer and cancer targets with the technological advances in drug discovery described below will accelerate the next generation of small-molecule drugs in oncology.

Figure 1.

Scientific and technical advances in biology, chemistry, and data science over the past two decades have driven the development of novel first-in-class drugs and the evolution of best-in-class drugs in oncology. ctDNA, circulating tumor DNA; DepMap, Cancer Dependency Map; DL, deep learning; FEP, free energy perturbation; GEMM, genetically engineered mouse model; LTS MD, long time-scale molecular dynamics; PDB, Protein Data Bank; PROTAC, proteolysis-targeting chimeras. Herceptin is manufactured by Genentech, Gleevec by Novartis, Iressa by AstraZeneca, Zelboraf by Genentech, Xalkori by Pfizer, Imbruvica by Pharmacyclics/AbbVie and Janssen, Zykadia by Novartis, Tagrisso by AstraZeneca, Vitrakvi by Bayer, Lorbrena by Pfizer, and Lumakras by Amgen.

Figure 2.

Databases and visualization tools for molecular characterization of human tumors and tumor cell lines. DepMap visualization for the PIK3CA gene indicating that cell lines with functional/activating PIK3CA mutations are dependent on PIK3CA for proliferation (16, 176). RNA-seq, RNA sequencing; WGS, whole-genome sequencing; WT, wild-type.

Figure 3.

The evolution of ALK inhibitors to treat ALK⁺ NSCLC. Kaplan–Meier plot illustrating the improvement in PFS of crizotonib vs. chemotherapy (143) on the left and lorlatinib vs. crizotinib (147) on the right. Reprinted with permission from NEJM. CI, confidence interval; SBDD, structure-based drug design.

Figure 4.

Integration of biology, chemistry, and data science is required to support the identification of novel targets and develop optimized, high-quality drug candidates. SBDD, structure-based drug design.

Figure 5.

The majority of targeted therapies serve patient populations of <10,000. Circles represent precision medicines against the indicated target, and colors represent tumor type as shown in the legend. Source: Boston Consulting Group analysis of Decision Resources Group epidemiology, ClinicalTrials.gov, FDA labels, and company websites. Data were gathered for approved precision oncology assets labeled according to their biological target and overall response rate (ORR) vs. prevalence of the relevant metastatic cancer. In instances in which there were several assets approved with the same biological target, ORR was based on the drug with the strongest response. AML, acute myelogenous leukemia; BCC, basal cell carcinoma; CLL, chronic lymphocytic leukemia; CML, chronic myelogenous leukemia; CRC, colorectal cancer; FL, follicular lymphoma; GIST, gastrointestinal stromal tumor; HCC, hepatocellular carcinoma; MCL, mantle cell lymphoma; MM, multiple myeloma; MZL, marginal zone lymphoma; NSCLC, non–small cell lung cancer; RCC, renal cell carcinoma; STS, soft tissue sarcoma; TGCT, tenosynovial giant cell tumor; WM, Waldenstrom macroglobulinemia.