Pharmacogenomic Biomarkers in US FDA-Approved Drug Labels (2000-2020).

Pharmacogenomics (PGx) is a key subset of precision medicine that relates genomic variation to individual response to pharmacotherapy. We assessed longitudinal trends in US FDA approval of new drugs labeled with PGx information. Drug labels containing PGx information were obtained from Drugs@FDA and guidelines from PharmGKB were used to compare the actionability of PGx information in drug labels across therapeutic areas. The annual proportion of new drug approvals with PGx labeling has increased by nearly threefold from 10.3% (n = 3) in 2000 to 28.2% (n = 11) in 2020. Inclusion of PGx information in drug labels has increased for all clinical areas over the last two decades but most prominently for cancer therapies, which comprise the largest proportion (75.5%) of biomarker-drug pairs for which PGx testing is required. Clinically actionable information was more frequently observed in biomarker-drug pairs associated with cancer drugs compared to those for other therapeutic areas (n = 92 (59.7%) vs. n = 62 (40.3%), p < 0.0051). These results suggest that further evidence is needed to support the clinical adoption of pharmacogenomics in non-cancer therapeutic areas.

The Implementation Process for Pharmacogenomic Testing for Cancer-Targeted Therapies.

Recent advances in genomic medicine have led to the availability of genomic tests that have the potential to improve population health, yet the process for obtaining these tests and getting them reimbursed by insurers has not been described. The objective of this study was to describe the process of ordering pharmacogenomic tests by interviewing providers, patients, and laboratories about cancer-related pharmacogenomic tests. We interviewed patients who were prescribed, providers who prescribed medications that should be guided by pharmacogenomic testing, and individuals from diagnostic laboratories. A total of 10 providers, 16 patients, and eight diagnostic laboratories described logistical and insurance issues relating to ordering and receiving pharmacogenomic tests and medications. We found that the process of ordering pharmacogenomic tests is time-consuming, expensive, and complex. Ordering pharmacogenomic tests is quite different across institutions. Even in the same institution, multiple providers can order the test. Once the provider places the order for the pharmacogenomic test, the laboratory receives the request and usually begins testing without knowing how the test will be paid for. Next, the laboratory completes the pharmacogenomic testing and the results of the tests are reported to providers, patients, or placed directly in the medical record. In conclusion, processes related to ordering and obtaining insurance coverage for pharmacogenomic tests varies greatly across institutions and is time-consuming.

Insurance Coverage Policies for Pharmacogenomic and Multi-Gene Testing for Cancer.

Insurance coverage policies are a major determinant of patient access to genomic tests. The objective of this study was to examine differences in coverage policies for guideline-recommended pharmacogenomic tests that inform cancer treatment. We analyzed coverage policies from eight Medicare contractors and 10 private payers for 23 biomarkers (e.g., HER2 and EGFR) and multi-gene tests. We extracted policy coverage and criteria, prior authorization requirements, and an evidence basis for coverage. We reviewed professional society guidelines and their recommendations for use of pharmacogenomic tests. Coverage for KRAS, EGFR, and BRAF tests were common across Medicare contractors and private payers, but few policies covered PML/RARA, CD25, or G6PD. Thirteen payers cover multi-gene tests for nonsmall lung cancer, citing emerging clinical recommendations. Coverage policies for single and multi-gene tests for cancer treatments are consistent among Medicare contractors despite the lack of national coverage determinations. In contrast, coverage for these tests varied across private payers. Patient access to tests is governed by prior authorization among eight private payers. Substantial variations in how payers address guideline-recommended pharmacogenomic tests and the common use of prior authorization underscore the need for additional studies of the effects of coverage variation on cancer care and patient outcomes.

Access to Guideline-Recommended Pharmacogenomic Tests for Cancer Treatments: Experience of Providers and Patients.

Genomic tests are the fastest growing sector in medicine and medical science, yet there remains a dearth of research on access to pharmacogenomic tests and medications. The objective of this study is to explore providers' and patients' experiences and views on test access as well as strategies used for gaining access. We interviewed clinicians who prescribed medications that should be guided by pharmacogenomic testing and patients who received those prescriptions. We organized the themes into the four dimensions suggested by the World Health Organization framework on access to medications and health technologies. Guideline-recommended pharmacogenomic tests for cancer care are generally available, although the timeliness of return of test results is sometimes suboptimal. Accessibility of pharmacogenomic tests is made challenging by the process of ordering pharmacogenomic tests, which is time-consuming. Affordability is a barrier to some patients as expressed by both providers and patients, who noted that the cost of pharmacogenomic tests and medications is high. Acceptability of the tests is high as both providers and patients view the tests positively. Understanding challenges to accessing pharmacogenomic tests will allow policymakers to develop policies that streamline access to genomics-based technologies to improve population health.

False Dichotomies and Health Policy Research Designs: Randomized Trials Are Not Always the Answer.

Some medical scientists argue that only data from randomized controlled trials (RCTs) are trustworthy. They claim data from natural experiments and administrative data sets are always spurious and cannot be used to evaluate health policies and other population-wide phenomena in the real world. While many acknowledge biases caused by poor study designs, in this article we argue that several valid designs using administrative data can produce strong findings, particularly the interrupted time series (ITS) design. Many policy studies neither permit nor require an RCT for cause-and-effect inference. Framing our arguments using Campbell and Stanley's classic research design monograph, we show that several "quasi-experimental" designs, especially interrupted time series (ITS), can estimate valid effects (or non-effects) of health interventions and policies as diverse as public insurance coverage, speed limits, hospital safety programs, drug abuse regulation and withdrawal of drugs from the market. We further note the recent rapid uptake of ITS and argue for expanded training in quasi-experimental designs in medical and graduate schools and in post-doctoral curricula.

A meta-analysis assessing all-cause mortality in HIV-exposed uninfected compared with HIV-unexposed uninfected infants and children.

OBJECTIVE: Conduct a meta-analysis examining differential all-cause mortality rates between HIV-exposed uninfected (HEU) infants and children as compared with their HIV-unexposed uninfected (HUU) counterparts. DESIGN: Meta-analysis summarizing the difference in mortality between HEU and HUU infants and children. Reviewed studies comparing children in the two groups for all-cause mortality, in any setting, from 1994 to 2016 from six databases. METHODS: Meta-analyses were done estimating overall mortality comparing the two groups, stratified by duration of follow-up time from birth (0-12, 12-24 and >24 months) and by year enrollment ended in each study: less than 2002 compared with at least 2002, when single-dose nevirapine for prevention of mother-to-child transmission (PMTCT) commenced in low-income and middle-income countries. RESULTS: Included 22 studies, for a total of 29 212 study participants [n = 8840 (30.3%) HEU; n = 20 372 (37.7%) HUU]. Random effects models showed HEU had a more than 70% increased risk of mortality vs. HUU. Stratifying by age showed that HEU vs. HUU had a significant 60-70% increased risk of death at every age strata. There was a significant 70% increase in the risk of mortality between groups before the implementation of PMTCT, which remained after 2002 [risk ratio: 1.46; 95% confidence interval (CI): 1.14-1.87], when the availability of PMTCT services was widespread, suggesting that prenatal antiretroviral therapy, and healthier mothers, does not fully eliminate this increased risk in mortality. CONCLUSION: We show a consistent increase risk of mortality for HEU vs. HUU infants and children. Longitudinal research is needed to elucidate underlying mechanisms, such as maternal and infant health status and breast feeding practices, which may help explain these differences in mortality.