Pharmacogenomics in cardiology--genetics and drug response: 10 years of progress

Implementation of pharmacogenomic testing in oncology care (PhOCus): study protocol of a pragmatic, randomized clinical trial

BACKGROUND

Many cancer patients who receive chemotherapy experience adverse drug effects. Pharmacogenomics (PGx) has promise to personalize chemotherapy drug dosing to maximize efficacy and safety. Fluoropyrimidines and irinotecan have well-known germline PGx associations. At our institution, we have delivered PGx clinical decision support (CDS) based on preemptively obtained genotyping results for a large number of non-oncology medications since 2012, but have not previously evaluated the utility of this strategy for patients initiating anti-cancer regimens. We hypothesize that providing oncologists with preemptive germline PGx information along with CDS will enable individualized dosing decisions and result in improved patient outcomes.

METHODS

Patients with oncologic malignancies for whom fluoropyrimidine and/or irinotecan-inclusive therapy is being planned will be enrolled and randomly assigned to PGx and control arms. Patients will be genotyped in a clinical laboratory across panels that include actionable variants in UGT1A1 and DPYD. For PGx arm patients, treating providers will be given access to the patient-specific PGx results with CDS prior to treatment initiation. In the control arm, genotyping will be deferred, and dosing will occur as per usual care. Co-primary endpoints are dose intensity deviation rate (the proportion of patients receiving dose modifications during the first treatment cycle), and grade ⩾3 treatment-related toxicities throughout the treatment course. Additional study endpoints will include cumulative drug dose intensity, progression-free survival, dosing of additional PGx supportive medications, and patient-reported quality of life and understanding of PGx.

DISCUSSION

Providing a platform of integrated germline PGx information may promote personalized chemotherapy dosing decisions and establish a new model of care to optimize oncology treatment planning.

Artificial Intelligence, Machine Learning, and Cardiovascular Disease

Artificial intelligence (AI)-based applications have found widespread applications in many fields of science, technology, and medicine. The use of enhanced computing power of machines in clinical medicine and diagnostics has been under exploration since the 1960s. More recently, with the advent of advances in computing, algorithms enabling machine learning, especially deep learning networks that mimic the human brain in function, there has been renewed interest to use them in clinical medicine. In cardiovascular medicine, AI-based systems have found new applications in cardiovascular imaging, cardiovascular risk prediction, and newer drug targets. This article aims to describe different AI applications including machine learning and deep learning and their applications in cardiovascular medicine. AI-based applications have enhanced our understanding of different phenotypes of heart failure and congenital heart disease. These applications have led to newer treatment strategies for different types of cardiovascular diseases, newer approach to cardiovascular drug therapy and postmarketing survey of prescription drugs. However, there are several challenges in the clinical use of AI-based applications and interpretation of the results including data privacy, poorly selected/outdated data, selection bias, and unintentional continuance of historical biases/stereotypes in the data which can lead to erroneous conclusions. Still, AI is a transformative technology and has immense potential in health care.

Deep learning in pharmacogenomics: from gene regulation to patient stratification

This Perspective provides examples of current and future applications of deep learning in pharmacogenomics, including: identification of novel regulatory variants located in noncoding domains of the genome and their function as applied to pharmacoepigenomics; patient stratification from medical records; and the mechanistic prediction of drug response, targets and their interactions. Deep learning encapsulates a family of machine learning algorithms that has transformed many important subfields of artificial intelligence over the last decade, and has demonstrated breakthrough performance improvements on a wide range of tasks in biomedicine. We anticipate that in the future, deep learning will be widely used to predict personalized drug response and optimize medication selection and dosing, using knowledge extracted from large and complex molecular, epidemiological, clinical and demographic datasets.

Implementation of a multidisciplinary pharmacogenomics clinic in a community health system

PURPOSE

The development and implementation of a multidisciplinary pharmacogenomics clinic within the framework of an established community-based medical genetics program are described.

SUMMARY

Pharmacogenomics is an important component of precision medicine that holds considerable promise for pharmacotherapy optimization. As part of the development of a health system-wide integrated pharmacogenomics program, in early 2015 Northshore University Health-System established a pharmacogenomics clinic run by a multidisciplinary team including a medical geneticist, a pharmacist, a nurse practitioner, and genetic counselors. The team identified five key program elements: (1) a billable-service provider, (2) a process for documentation of relevant medication and family histories, (3) personnel with the knowledge required to interpret pharmacogenomic results, (4) personnel to discuss risks, benefits, and limitations of pharmacogenomic testing, and (5) a mechanism for reporting results. The most important program component is expert interpretation of genetic test results to provide clinically useful information; pharmacists are well positioned to provide that expertise. At the Northshore University HealthSystem pharmacogenomics clinic, patient encounters typically entail two one-hour visits and follow a standardized workflow. At the first visit, pharmacogenomics-focused medication and family histories are obtained, risks and benefits of genetic testing are explained, and a test sample is collected; at the second visit, test results are provided along with evidence-based pharmacotherapy recommendations.

CONCLUSION

A multidisciplinary clinic providing genotyping and related services can facilitate the integration of pharmacogenomics into clinical care and meet the needs of early adopters of precision medicine.