Objectives: Apart from irrational polypharmacy, drug-drug interactions (DDIs) and potentially inappropriate medications for elderly patients, there are numerous challenges referring to psychopharmacological treatment in this patient subgroup prescribed psychotropic agents. Specifically, pharmacokinetics and pharmacodynamics essentially differ in elderly patients, complicating medication selection and dosing.

Methods: Pharmacokinetics involves absorption, distribution, metabolism, and elimination of the medication. Elderly patients often present distinct patterns of medication absorption as well as reduced elimination (due to decreased kidney function). DDIs are another major confounder of medication metabolism. This is particularly

common in elderly patients, as one out of two elderly patients with mental disorders receive polypharmacy which in turn requires careful dose adjustment. Medication adherence is an additional challenge in patients with mental disorders, further complicating pharmacotherapy.

Results: We discuss the benefits of therapeutic drug monitoring (TDM), i.e. the regular measuring of drug concentrations in plasma of patients prescribed psychotropic agents. TDM is a valuable personalized treatment tool; specifically, it allows the determination of the minimum effective dosage also improving safety outcomes. Basic TDM principles, such as steady-state, trough levels and therapeutic reference ranges in the context of elderly patients’ pharmacological treatment will be discussed.

Discussion: The use of TDM is of established clinical value in particularly vulnerable patient subgroups such as elderly patients. Summarizing practical recommendations for TDM in clinical routine we aim to enhance its use ultimately enabling an improvement of treatment effectiveness and safety outcomes in elderly patients prescribed psychotropic agents.

The interplay between genetic and environmental factors plays a significant role in interindividual variation in immune and inflammatory responses. The availability of high-throughput low-cost genotyping and next-generation sequencing has revolutionized our ability to identify human genetic variation and understand how this varies within and between populations, and the relationship with disease. In this review, we explore the potential of genomics for patient benefit, specifically in the diagnosis, prognosis and treatment of inflammatory and immune-related diseases. We summarize the knowledge arising from genetic and functional genomic approaches, and the opportunity for personalized medicine. The review covers applications in infectious diseases, rare immunodeficiencies and autoimmune diseases, illustrating advances in diagnosis and understanding risk including use of polygenic risk scores. We further explore the application for patient stratification and drug target prioritization. The review highlights a key challenge to the field arising from the lack of sufficient representation of genetically diverse populations in genomic studies. This currently limits the clinical utility of genetic-based diagnostic and risk-based applications in non-Caucasian populations. We highlight current genome projects, initiatives and biobanks from diverse populations and how this is being used to improve healthcare globally by improving our understanding of genetic susceptibility to diseases and regional pathogens such as malaria and tuberculosis. Future directions and opportunities for personalized medicine and wider application of genomics in health care are described, for the benefit of individual patients and populations worldwide.

Personalized medicine has been progressively implemented in several diagnostic and therapeutic patients’ algorithms, based on the common assumption that tailoring interventions, practices, and/or therapies to individual patients’ clinical, biological, epidemiological, and genetic characteristics would optimize their effectiveness and reduce adverse effects. The potential benefit of the precision medicine approach has been recently considered for possible implementation in the field of infection prevention and control. The commentary explores available evidence and assesses possible future scenarios where, through advanced modeling approaches, we would be able to provide personalized prediction algorithms identifying at-risk patients who deserve the implementation of tailored preventive measures.

Observational studies are notoriously susceptible to bias, and parallel-group randomized trials are important to identify the best overall treatment for eligible patients. Yet, such trials can be expected to be a misleading indicator of the best treatment for some subgroups or individual patients. In selected circumstances, patients can be treated in n-of-1 trials to address the inherent heterogeneity of treatment response in clinical populations. Such trials help to accomplish the ultimate goal of all biomedical research, to optimize the care of individual patients.

The single largest contributor to human mortality is cardiovascular disease, the top risk factor for which is hypertension (HTN). The last two decades have placed much emphasis on the identification of genetic factors contributing to HTN. As a result, over 1,500 genetic alleles have been associated with human HTN. Mapping studies using genetic models of HTN have yielded hundreds of blood pressure (BP) loci but their individual effects on BP are minor, which limits opportunities to target them in the clinic. The value of collecting genome-wide association data is evident in ongoing research, which is beginning to utilize these data at individual-level genetic disparities combined with artificial intelligence (AI) strategies to develop a polygenic risk score (PRS) for the prediction of HTN. However, PRS alone may or may not be sufficient to account for the incidence and progression of HTN because genetics is responsible for <30% of the risk factors influencing the etiology of HTN pathogenesis. Therefore, integrating data from other nongenetic factors influencing BP regulation will be important to enhance the power of PRS. One such factor is the composition of gut microbiota, which constitute a more recently discovered important contributor to HTN. Studies to-date have clearly demonstrated that the transition from normal BP homeostasis to a state of elevated BP is linked to compositional changes in gut microbiota and its interaction with the host. Here, we first document evidence from studies on gut dysbiosis in animal models and patients with HTN followed by a discussion on the prospects of using microbiota data to develop a metagenomic risk score (MRS) for HTN to be combined with PRS and a clinical risk score (CRS). Finally, we propose that integrating AI to learn from the combined PRS, MRS and CRS may further enhance predictive power for the susceptibility and progression of HTN.

Genetics has been an important tool for discovering new aspects of biology across life. In humans, there is growing momentum behind the application of this knowledge to drive innovation in clinical care, most notably through developments in precision medicine. Nowhere has the impact of genetics on clinical practice been more striking than in the field of rare disorders. For most of these conditions, individual disease susceptibility is influenced by DNA sequence variation in a single or a small number of genes. In contrast, most common disorders are multifactorial and are caused by a complex interplay of multiple genetic, environmental and stochastic factors. The longstanding division of human disease genetics into rare and common components has obscured the continuum of human traits and echoes aspects of the century-old debate between the Mendelian and biometric views of human genetics. In this article, we discuss the differences in data and concepts between rare and common disease genetics. Opportunities to unify these two areas are noted and the importance of adopting a holistic perspective that integrates diverse genetic and environmental factors is discussed.

An ever-expanding annotation of the human genome sequence continues to promise a new era of precision medicine. Advances in knowledge management and the ability to leverage genetic information to make clinically relevant, predictive, diagnostic, and targeted therapeutic choices offer the ability to improve patient outcomes and reduce the overall cost of healthcare. However, numerous barriers have resulted in a modest start to the clinical use of genetics at scale. Examples of successful deployments include oncologic disease treatment with targeted prescribing; however, even in these cases, genome-informed decision-making has yet to achieve standard of care in most major healthcare systems. In the last two decades, advances in genetic testing, therapeutic coverage, and clinical decision support have resulted in early-stage adoption of pharmacogenomics – the use of genetic information to routinely determine the safety and efficacy profile of specific medications for individuals. Here, through their complicated histories, we review the current state of pharmacogenomic testing technologies, the information tools that can unlock clinical utility, and value-driving implementation strategies that represent the future of pharmacogenomics-enabled healthcare decision-making. We conclude with real-world economic and clinical outcomes from a full-scale deployment and ultimately provide insight into potential tipping points for global adoption, including recent lessons from the rapid scale-up of high-volume test delivery during the global SARS-CoV2 epidemic.

Several data indicate that the success of pharmacological treatment in major depressive disorder (MDD) is still unsatisfactory. The determination of the optimal treatment generally requires multiple trials with different treatments, with the sobering observation that the more treatments tried without success, the less likely a successful outcome, with the result of a long unremitted disease, worse long term prognosis, increased rates of side effects, and important medical, social and economic burden. The reasons for the low response and remission rates are multiple and depend on environmental and biological factors intrinsic to the disease and drug treatments. Pharmacogenetic (PG) tests have the potential to increase efficacy predicting outcome and to reduce antidepressant discontinuation due to side effects. Several studies investigated the utility of PG tests for antidepressants in MDD with interesting but contrasting results. To date most of them are observational studies with no comparator group, and few are randomized controlled trials (RCTs). Several limitations concerning study design, generalization of results, duration of trials, patients group studied, and cost-effectiveness ratio were found, and a number of barriers have been noted in the adoption of PG tests into clinical practice. Despite some preliminary positive results, there is the need for larger and longer‐term RCT studies, with the goal to capture the real impact of PG tests, also with stratified analysis concerning MDD features in terms of severity and antidepressant treatment failures in different ethnicity cohorts.