CYP2D6 phenotypes and opioid metabolism: the path to personalized analgesia.

INTRODUCTION: Opioids play a fundamental role in chronic pain, especially considering when 1 of 5 Europeans adults, even more in older females, suffer from it. However, half of them do not reach an adequate pain relief. Could pharmacogenomics help to choose the most appropriate analgesic drug? AREAS COVERED: The objective of the present narrative review was to assess the influence of cytochrome P450 2D6 (CYP2D6) phenotypes on pain relief, analgesic tolerability, and potential opioid misuse. Until December 2021, a literature search was conducted through the MEDLINE, PubMed database, including papers from the last 10 years. CYP2D6 plays a major role in metabolism that directly impacts on opioid (tramadol, codeine, or oxycodone) concentration with differences between sexes, with a female trend toward poorer pain control. In fact, CYP2D6 gene variants are the most actionable to be translated into clinical practice according to regulatory drug agencies and international guidelines. EXPERT OPINION: CYP2D6 genotype can influence opioids' pharmacokinetics, effectiveness, side effects, and average opioid dose. This knowledge needs to be incorporated in pain management. Environmental factors, psychological together with genetic factors, under a sex perspective, must be considered when you are selecting the most personalized pain therapy for your patients.

Laying the groundwork for the Biobank of Rare Malignant Neoplasms at the service of the Hellenic Network of Precision Medicine on Cancer.

Biobanks constitute an integral part of precision medicine. They provide a repository of biospecimens that may be used to elucidate the pathophysiology, support diagnoses, and guide the treatment of diseases. The pilot biobank of rare malignant neoplasms has been established in the context of the Hellenic Network of Precision Medicine on Cancer and aims to enhance future clinical and/or research studies in Greece by collecting, processing, and storing rare malignant neoplasm samples with associated data. The biobank currently comprises 553 samples; 384 samples of hematopoietic and lymphoid tissue malignancies, 72 samples of pediatric brain tumors and 97 samples of malignant skin neoplasms. In this article, sample collections and their individual significance in clinical research are described in detail along with computational methods developed specifically for this project. A concise review of the Greek biobanking landscape is also delineated, in addition to recommended technologies, methodologies and protocols that were integrated during the creation of the biobank. This project is expected to re­enforce current clinical and research studies, introduce advances in clinical and genetic research and potentially aid in future targeted drug discovery. It is our belief that the future of medical research is entwined with accessible, effective, and ethical biobanking and that our project will facilitate research planning in the '­omic' era by contributing high­quality samples along with their associated data.

Sex-tailored pharmacology and COVID-19: Next steps towards appropriateness and health equity.

Making gender bias visible allows to fill the gaps in knowledge and understand health records and risks of women and men. The coronavirus disease 2019 (COVID-19) pandemic has shown a clear gender difference in health outcomes. The more severe symptoms and higher mortality in men as compared to women are likely due to sex and age differences in immune responses. Age-associated decline in sex steroid hormone levels may mediate proinflammatory reactions in older adults, thereby increasing their risk of adverse outcomes, whereas sex hormones and/or sex hormone receptor modulators may attenuate the inflammatory response and provide benefit to COVID-19 patients. While multiple pharmacological options including anticoagulants, glucocorticoids, antivirals, anti-inflammatory agents and traditional Chinese medicine preparations have been tested to treat COVID-19 patients with varied levels of evidence in terms of efficacy and safety, information on sex-targeted treatment strategies is currently limited. Women may have more benefit from COVID-19 vaccines than men, despite the occurrence of more frequent adverse effects, and long-term safety data with newly developed vectors are eagerly awaited. The prevalent inclusion of men in randomized clinical trials (RCTs) with subsequent extrapolation of results to women needs to be addressed, as reinforcing sex-neutral claims into COVID-19 research may insidiously lead to increased inequities in health care. The huge worldwide effort with over 3000 ongoing RCTs of pharmacological agents should focus on improving knowledge on sex, gender and age as pillars of individual variation in drug responses and enforce appropriateness.

Personalized mechanical ventilation in acute respiratory distress syndrome.

A personalized mechanical ventilation approach for patients with adult respiratory distress syndrome (ARDS) based on lung physiology and morphology, ARDS etiology, lung imaging, and biological phenotypes may improve ventilation practice and outcome. However, additional research is warranted before personalized mechanical ventilation strategies can be applied at the bedside. Ventilatory parameters should be titrated based on close monitoring of targeted physiologic variables and individualized goals. Although low tidal volume (VT) is a standard of care, further individualization of VT may necessitate the evaluation of lung volume reserve (e.g., inspiratory capacity). Low driving pressures provide a target for clinicians to adjust VT and possibly to optimize positive end-expiratory pressure (PEEP), while maintaining plateau pressures below safety thresholds. Esophageal pressure monitoring allows estimation of transpulmonary pressure, but its use requires technical skill and correct physiologic interpretation for clinical application at the bedside. Mechanical power considers ventilatory parameters as a whole in the optimization of ventilation setting, but further studies are necessary to assess its clinical relevance. The identification of recruitability in patients with ARDS is essential to titrate and individualize PEEP. To define gas-exchange targets for individual patients, clinicians should consider issues related to oxygen transport and dead space. In this review, we discuss the rationale for personalized approaches to mechanical ventilation for patients with ARDS, the role of lung imaging, phenotype identification, physiologically based individualized approaches to ventilation, and a future research agenda.

Inaugural Pharmacogenomics Access and Reimbursement Symposium.

The Pharmacogenomics Access & Reimbursement Symposium, a landmark event presented by the Golden Helix Foundation and the Pharmacogenomics Access & Reimbursement Coalition, was a 1-day interactive meeting comprised of plenary keynotes from thought leaders across healthcare that focused on value-based strategies to improve patient access to personalized medicine. Stakeholders including patients, healthcare providers, industry, government agencies, payer organizations, health systems and health policy organizations convened to define opportunities to improve patient access to personalized medicine through best practices, successful reimbursement models, high quality economic evaluations and strategic alignment. Session topics included health technology assessment, health economics, health policy and value-based payment models and innovation.

Phenotypes and personalized medicine in the acute respiratory distress syndrome.

Although the acute respiratory distress syndrome (ARDS) is well defined by the development of acute hypoxemia, bilateral infiltrates and non-cardiogenic pulmonary edema, ARDS is heterogeneous in terms of clinical risk factors, physiology of lung injury, microbiology, and biology, potentially explaining why pharmacologic therapies have been mostly unsuccessful in treating ARDS. Identifying phenotypes of ARDS and integrating this information into patient selection for clinical trials may increase the chance for efficacy with new treatments. In this review, we focus on classifying ARDS by the associated clinical disorders, physiological data, and radiographic imaging. We consider biologic phenotypes, including plasma protein biomarkers, gene expression, and common causative microbiologic pathogens. We will also discuss the issue of focusing clinical trials on the patient's phase of lung injury, including prevention, administration of therapy during early acute lung injury, and treatment of established ARDS. A more in depth understanding of the interplay of these variables in ARDS should provide more success in designing and conducting clinical trials and achieving the goal of personalized medicine.

Practicing precision medicine with intelligently integrative clinical and multi-omics data analysis.

Precision medicine aims to empower clinicians to predict the most appropriate course of action for patients with complex diseases like cancer, diabetes, cardiomyopathy, and COVID-19. With a progressive interpretation of the clinical, molecular, and genomic factors at play in diseases, more effective and personalized medical treatments are anticipated for many disorders. Understanding patient's metabolomics and genetic make-up in conjunction with clinical data will significantly lead to determining predisposition, diagnostic, prognostic, and predictive biomarkers and paths ultimately providing optimal and personalized care for diverse, and targeted chronic and acute diseases. In clinical settings, we need to timely model clinical and multi-omics data to find statistical patterns across millions of features to identify underlying biologic pathways, modifiable risk factors, and actionable information that support early detection and prevention of complex disorders, and development of new therapies for better patient care. It is important to calculate quantitative phenotype measurements, evaluate variants in unique genes and interpret using ACMG guidelines, find frequency of pathogenic and likely pathogenic variants without disease indicators, and observe autosomal recessive carriers with a phenotype manifestation in metabolome. Next, ensuring security to reconcile noise, we need to build and train machine-learning prognostic models to meaningfully process multisource heterogeneous data to identify high-risk rare variants and make medically relevant predictions. The goal, today, is to facilitate implementation of mainstream precision medicine to improve the traditional symptom-driven practice of medicine, and allow earlier interventions using predictive diagnostics and tailoring better-personalized treatments. We strongly recommend automated implementation of cutting-edge technologies, utilizing machine learning (ML) and artificial intelligence (AI) approaches for the multimodal data aggregation, multifactor examination, development of knowledgebase of clinical predictors for decision support, and best strategies for dealing with relevant ethical issues.

The future is now? Clinical and translational aspects of "Omics" technologies.

Big data has become a central part of medical research, as well as modern life generally. "Omics" technologies include genomics, proteomics, microbiomics and increasingly other omics. These have been driven by rapid advances in laboratory techniques and equipment. Crucially, improved information handling capabilities have allowed concepts such as artificial intelligence and machine learning to enter the research world. The COVID-19 pandemic has shown how quickly information can be generated and analyzed using such approaches, but also showed its limitations. This review will look at how "omics" has begun to be translated into clinical practice. While there appears almost limitless potential in using big data for "precision" or "personalized" medicine, the reality is that this remains largely aspirational. Oncology is the only field of medicine that is widely adopting such technologies, and even in this field uptake is irregular. There are practical and ethical reasons for this lack of translation of increasingly affordable techniques into the clinic. Undoubtedly, there will be increasing use of large data sets from traditional (e.g. tumor samples, patient genomics) and nontraditional (e.g. smartphone) sources. It is perhaps the greatest challenge of the health-care sector over the coming decade to integrate these resources in an effective, practical and ethical way.