Some of the most emerging and promising tools being developed and increasingly being used in the health care-related sectors include data warehouse, that is a repository of historical data from data warehouses to data refineries for refining the crude data dubbed as the “oil of the digital era” into valuable data all the way from research to clinical use [16, 17]. Because the data in the raw form is enormous, complex, lacks structure and standardization combined with interoperability issues, compliance issues and ethical challenges, the data refineries are envisioned to bridge the gap for refining and distilling the data on its journey from research to clinical utility for the benefits of the patients, including the stakeholders.
To navigate the costly and complex landscape of therapeutic drugs from basic research to clinical use hinges on integrating multi-disciplinary approaches. Because of different goals of the stakeholders, it has been historically challenging to strike a common chord that resonates across the whole ecosystem. Over the last few years, there has been a paradigm shift due to many factors such as the high cost of drug development, lengthy approval process, closer collaborations between academia and industries, integration of emerging technologies such as digital health, telehealth and wearables, gene editing, including big data, funding, education, and changes in government policies. The health benefits of panomics (genomics, proteomics, transcriptomics, metabolomics, epigenomics, ionomics and microbiomics) and the increasing use of panomics in personalizing medicine are emerging and promising in the treatment of diseases such as cancer, cardiovascular and gastrointestinal disorders [18, 19]. The integral role of “gut” microbiome in health and in treating many diseases, including cancer is beginning to emerge as demonstrated by immune checkpoint inhibitor therapy [20, 21]. While panomics addresses many of the precision medicine treatment benefits, it falls short in addressing issues such as the multi-morbidity, impact of the disease on the patients’ lives, their adaptability to the disease or other existing diseases, their family, their social life and their community life. Personomics is thus envisioned to bridge the gap between panomics and the patients’ personal or an individual’s circumstances . This “echoes” with the words of Sir William Osler: “the good physician treats the disease; the great physician treats the patient who has the disease” .
The Precision Medicine Initiative (PMI) launched in 2015  has been building to a “crescendo” and its impact on drug development, clinical trials and in personalizing the treatment for therapeutic efficacy, maximum safety, higher durable response, longevity and higher quality of life is emerging. Over the last few years, the FDA has emphasized the use of real-world data (RWD) and real-world evidence (RWE) to modernize clinical trials, an advancement made possible by the 21st Century Cures Act [25, 26]. With real-world data and real-evidence, researchers will be able to go beyond the scope of traditional trials, transition to a “hybrid” trial that is dynamic, providing insights from information collected in clinical care. As an example, the FDA in April 2019 approved a supplemental New Drug Application based on data extracted from EHR and post-marketing reports of the real-world use of Pfizer’s drug, IBRANCE (Palbociclib) to expand the indication for in combination with Fulvestrant to include men with hormone receptor positive (HR+), human EGFR 2 negative (HER2−) advanced or metastatic breast cancer, for the treatment of breast cancer in men [27, 28]. The former FDA Commissioner, Dr. Scott Gottlieb stated : “the EHRs and other data sources, paired with advances in machine learning, will be crucial for architecting the next generation of successful clinical trials.
To address many of the challenges of implementing genomics medicine for routine use, the NIH funded IGNITE Network with the goals of integrating genomic data into EHR . The IGNITE Network deploys plethoric “tools” for “Point-of-Care Decisions”, genetic markers for disease risk prediction including prevention, tools about family history data, pharmacogenomics data and refinement of disease diagnosis. Similarly, IBM Watson Health in collaboration with Brigham and Women Hospital and Vanderbilt University Medical Center has been pursuing the use of artificial intelligence for supporting precision medicine, to enhance patient safety, to nurture health equity, to expand and improve EHR usability . Furthermore, the Watson Studio and Watson Knowledge Catalog has the data refinery tool for processing and transforming large amounts of raw data into valuable and clinical useful information for analytics. Several governments across the world, organizations, academia and institutes have created open access networks such as the Cancer Biomedical Informatics Grid (caBIG) and the Cancer Translational Research Informatics Grid (caTrip) for the caBIG project with a focus and a mission about driving translational research and improving the patient outcome by linking network of researchers, patients and physicians . Similarly government and non-government sponsored programs have been established and they have been mushrooming globally such as the ICPerMed and the ECMC in the UK that support biotech and pharmaceutical companies to develop drugs in oncology through strategic partnerships . Examples of programs in the USA include: the National Center for Advancing Translational Sciences (NCATS) at the National Institutes of Health (NIH), the NCI-MATCH, a precision medicine cancer treatment clinical trial that is co-led by National Cancer Institute (NCI) and the ECOG-ACRIN Cancer Research Group. In the NCI-MATCH trial, patients received therapy based on the genetic changes found in their tumor as exemplified by the results from Arm H of the study demonstrated that treatment with a “cocktail” of dabrafenib and trametinib, designed to target cancers that have specific BRAF gene mutations, was effective in a trial of 35 patients having 17 distinct tumor types . Most recently, the studies  published by the Pan-cancer Analysis of Whole Genome (PCAWG) consortium involving whole genome sequencing of 2658 cancer genomes demonstrated new information about cancer drivers from 38 tumor types and identified potentially new targets for precision medicine.
The exploitation of such tools for the “Right-to-Try” experimental drugs in treating life threatening diseases such as cancer will most likely favor the outcome and mitigate the negative outcomes associated with the clinical use of experimental drugs. The outcome using experimental drug has the potential to be more favorable if combined (“cock-tail”) with an FDA-approved drug. Some of the other approaches include implementation of programs and policies that incorporate the interests of patients such as education, understanding of risks, second opinion about the therapy, expectations and costs due to potential complications, education of physicians about precision medicine and emerging tools, dosing, the use of EHR, systematic reporting of results that may be important for public health and safety. The sponsor should provide transparency and immediate notifications to the physician including the FDA about safety issues during the drug development stage and any manufacturing or supply issues. Due to the complexity of the informed consent form and lack of oversight by the FDA, it is important that an independent or a neutral body such as an IRB or an ethics committee is engaged in reviewing the consent procedures. The positive outcome of the treatment results will thus be a potential boon to young biotech and pharmaceutical companies facing the “valley of death” syndrome, struggling to raise funding or looking for partnerships or trying to build trust and credibility. Furthermore, a positive outcome has the potential to spawn new ventures or opportunities such as veterinary oncology and “outpatient” clinics.
It is envisioned that the use of new tools encompassing electronic medical records, personalized medicine, data refineries, artificial intelligence and machine learning, further testing of drugs, including adjuvant therapies or “cocktail” of drugs will favor the outcome of experimental drugs and may pave the way for indication expansion as exemplified by IBRANCE. Furthermore, the use of such tools are expected to: (i) accelerate drug development time; (ii) reduce drug development cost; (iii) lower the cost of drugs; (iv) improve the durable response; (v) reduce adverse effects such as hepato-and cardio-toxicity; (vi) improve longevity and quality of life of the patient.