The evidence landscape in precision medicine

Unveiling the future: precision pharmacovigilance in the era of personalized medicine

In the era of personalized medicine, pharmacovigilance faces new challenges and opportunities, demanding a shift from traditional approaches. This article delves into the evolving landscape of drug safety monitoring in the context of personalized treatments. We aim to provide a succinct reflection on the intersection of tailored therapeutic strategies and vigilant pharmacovigilance practices. We discuss the integration of pharmacogenetics in enhancing drug safety, illustrating how genetic profiling aids in predicting drug responses and adverse reactions. Emphasizing the importance of phase IV-post-marketing surveillance, we explore the limitations of pre-marketing trials and the necessity for a comprehensive approach to drug safety. The article discusses the pivotal role of pharmacogenetics in pre-exposure risk management and the redefinition of pharmacoepidemiological methods for post-exposure surveillance. We highlight the significance of integrating patient-specific genetic profiles in creating personalized medication leaflets and the use of advanced computational methods in data analysis. Additionally, we examine the ethical, privacy, and data security challenges inherent in precision medicine, emphasizing their implications for patient consent and data management.

A personalized pharmaco-epistatic network model of precision medicine

Precision medicine, the utilization of targeted treatments to address an individual's disease, relies on knowledge about the genetic cause of that individual's drug response. Here, we present a functional graph (FunGraph) theory to chart comprehensive pharmacogenetic architecture for each and every patient. FunGraph is the combination of functional mapping - a dynamic model for genetic mapping and evolutionary game theory guiding interactive strategies. It coalesces all pharmacogenetic factors into multilayer and multiplex networks that fully capture bidirectional, signed and weighted epistasis. It can visualize and interrogate how epistasis moves in the cell and how this movement leads to patient- and context-specific genetic architecture in response to organismic physiology. We discuss the future implementation of FunGraph to achieve precision medicine. Teaser: We present a functional graph (FunGraph) theory to draw a complete picture of pharmacogenetic architecture underlying interindividual variability in drug response. FunGraph can characterize how each gene acts and interacts with every other gene to mediate therapeutic response.

Popular deep learning algorithms for disease prediction: a review

Due to its automatic feature learning ability and high performance, deep learning has gradually become the mainstream of artificial intelligence in recent years, playing a role in many fields. Especially in the medical field, the accuracy rate of deep learning even exceeds that of doctors. This paper introduces several deep learning algorithms: Artificial Neural Network (NN), FM-Deep Learning, Convolutional NN and Recurrent NN, and expounds their theory, development history and applications in disease prediction; we analyze the defects in the current disease prediction field and give some current solutions; our paper expounds the two major trends in the future disease prediction and medical field-integrating Digital Twins and promoting precision medicine. This study can better inspire relevant researchers, so that they can use this article to understand related disease prediction algorithms and then make better related research.

Stakeholders Perceptions of Barriers to Precision Medicine Adoption in the United States

Despite evidence that precision medicine (PM) results in improved patient care, the broad adoption and implementation has been challenging across the United States (US). To better understand the perceived barriers associated with PM adoption, a quantitative survey was conducted across five stakeholders including medical oncologists, surgeons, lab directors, payers, and patients. The results of the survey reveal that stakeholders are often not aligned on the perceived challenges with PM awareness, education and reimbursement, with there being stark contrast in viewpoints particularly between clinicians, payers, and patients. The output of this study aims to help raise the awareness that misalignment on the challenges to PM adoption is contributing to broader lack of implementation that ultimately impacts patients. With better understanding of stakeholder viewpoints, we can help alleviate the challenges by focusing on multi-disciplinary education and awareness to ultimately improve patient outcomes.

Translational precision medicine: an industry perspective

In the era of precision medicine, digital technologies and artificial intelligence, drug discovery and development face unprecedented opportunities for product and business model innovation, fundamentally changing the traditional approach of how drugs are discovered, developed and marketed. Critical to this transformation is the adoption of new technologies in the drug development process, catalyzing the transition from serendipity-driven to data-driven medicine. This paradigm shift comes with a need for both translation and precision, leading to a modern Translational Precision Medicine approach to drug discovery and development. Key components of Translational Precision Medicine are multi-omics profiling, digital biomarkers, model-based data integration, artificial intelligence, biomarker-guided trial designs and patient-centric companion diagnostics. In this review, we summarize and critically discuss the potential and challenges of Translational Precision Medicine from a cross-industry perspective.

The European Union and personalised cancer medicine

Two recent policy documents by the European Union, 'Europe's Beating Cancer Plan' and its accompanying 'Conquering Cancer: Mission Possible' (CCMP), articulate broad policies aimed at reducing cancer mortality across Europe, for example, by promoting prevention and early detection. The focus for cancer treatment in these manifestos is the expansion of personalised cancer medicine (PCM). However, the CCMP document suggests that the uptake of PCM is "hampered by uncertainty about its outcomes". What are these outcomes and why this uncertainty? We address the limits of PCM in pathology-driven and pathology-agnostic PCM, briefly discussing the results of umbrella and basket trials. We suggest that the complexity, plasticity and genetic heterogeneity of advanced cancers will continue to thwart the impact of PCM, limiting it to specific pathologies, or rare subsets of them. Caution regarding the advancement of PCM is justified, and policymakers should be wary of the hype of lobbyists, who do not acknowledge the limits of PCM.

Patient-derived organoids as a predictive biomarker for treatment response in cancer patients

Effective predictive biomarkers are needed to enable personalized medicine and increase treatment efficacy and survival for cancer patients, thereby reducing toxic side effects and treatment costs. Patient-derived organoids (PDOs) enable individualized tumour response testing. Since 2018, 17 publications have examined PDOs as a potential predictive biomarker in the treatment of cancer patients. We review and provide a pooled analysis of the results regarding the use of PDOs in individualized tumour response testing, focusing on evidence for analytical validity, clinical validity and clinical utility. We identify future perspectives to accelerate the implementation of PDOs as a predictive biomarker in the treatment of cancer patients.

Pharmacoepidemiology: A time for a new multidisciplinary approach to precision medicine

The advent of the genomic age has created a rapid increase in complexity for the development and selection of drug treatments. A key component of precision medicine is the use of genetic information to improve therapeutic effectiveness of drugs and prevent potential adverse drug reactions. Pharmacoepidemiology, as a field, uses observational methods to evaluate the safety and effectiveness of drug treatments in populations. Pharmacoepidemiology by virtue of its focus, tradition, and research orientation can provide appropriate study designs and analysis methods for precision medicine. The objective of this manuscript is to demonstrate how pharmacoepidemiology can impact and shape precision medicine and serve as a reference for pharmacoepidemiologists interested in contributing to the science of precision medicine. This paper depicts the state of the science with respect to the need for pharmacoepidemiology and pharmacoepidemiological methods, tools and approaches for precision medicine; the need for and how pharmacoepidemiologists use their skills to engage with the precision medicine community; and recommendations for moving the science of precision medicine pharmacoepidemiology forward. We propose a new integrated multidisciplinary approach dedicated to the emerging science of precision medicine pharmacoepidemiology.

Multi-parametric characterization of drug effects on cells

We present here a novel multi-parametric approach for the characterization of multiple cellular features, using images acquired by high-throughput and high-definition light microscopy. We specifically used this approach for deep and unbiased analysis of the effects of a drug library on five cultured cell lines. The presented method enables the acquisition and analysis of millions of images, of treated and control cells, followed by an automated identification of drugs inducing strong responses, evaluating the median effect concentrations and those cellular properties that are most highly affected by the drug. The tools described here provide standardized quantification of multiple attributes for systems level dissection of complex functions in normal and diseased cells, using multiple perturbations. Such analysis of cells, derived from pathological samples, may help in the diagnosis and follow-up of treatment in patients.

A Biomarker-Centric Approach to Drug Discovery and Development: Lessons Learned from the Coronavirus Disease 2019 Pandemic

Faced with the health and economic consequences of the global spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the biomedical community came together to identify, diagnose, prevent, and treat the novel disease at breathtaking speeds. The field advanced from a publicly available viral genome to a commercialized globally scalable diagnostic biomarker test in less than 2 months, and first-in-human dosing with vaccines and repurposed antivirals followed shortly thereafter. This unprecedented efficiency was driven by three key factors: 1) international multistakeholder collaborations, 2) widespread data sharing, and 3) flexible regulatory standards tailored to meet the urgency of the situation. Learning from the remarkable success achieved during this public health crisis, we are proposing a biomarker-centric approach throughout the drug development pipeline. Although all therapeutic areas would benefit from end-to-end biomarker science, efforts should be prioritized to areas with the greatest unmet medical needs, including neurodegenerative diseases, chronic lower respiratory diseases, metabolic disorders, and malignant neoplasms. SIGNIFICANCE STATEMENT: Faced with the unprecedented threat of the severe acute respiratory syndrome coronavirus 2 pandemic, the biomedical community collaborated to develop a globally scalable diagnostic biomarker (viral DNA) that catalyzed therapeutic development at breathtaking speeds. Learning from this remarkable efficiency, we propose a multistakeholder biomarker-centric approach to drug development across therapeutic areas with unmet medical needs.

Precision medicine in follicular lymphoma: Focus on predictive biomarkers

Current care for patients with follicular lymphoma (FL) offers most of them long-term survival. Improving it further will require careful patient selection. This review focuses on predictive biomarkers (ie, those whose outcome correlations depend on the treatment strategy) in FL, because awareness of what patient subsets benefit most or least from each therapy will help in this task. The first part of this review aims to summarize what biomarkers are predictive in FL, the magnitude of the effect and the quality of the evidence. We find predictive biomarkers in the setting of (a) indication of active treatment, (b) front-line induction (use of anthracyline-based regimens, CHOP vs bendamustine, addition of rituximab), (c) post-(front-line)induction (rituximab maintenance, radioimmunotherapy), and (d) relapse (hematopoietic stem cell transplant) and targeted agents. The second part of this review discusses the challenges of precision medicine in FL, including (a) cost, (b) clinical relevance considerations, and (c) difficulties over the broad implementation of biomarkers. We then provide our view on what biomarkers may become used in the next few years. We conclude by underscoring the importance of assessing the potential predictiveness of available biomarkers to improve patient care but also that there is a long road ahead before reaching their broad implementation due to remaining scientific, technological, and economic hurdles.

Global clinical trial mobilization for COVID-19: higher, faster, stronger

•

COVID-19 has prompted vastly more clinical trials than previous health emergencies.

•

Trials started faster, are collectively larger, and are more diverse geographically.

•

The initial trials so far are mostly not funded by industry.

•

Lack of coordination will lead to inefficiency and duplication.

The clinical trial landscape for Coronavirus 2019 (COVID-19) is radically different from that of previous epidemics. Compared with H1N1, Ebola, and Zika, COVID-19 had an order of magnitude more clinical trials within the first 3 months following the declaration of a Public Health Emergency of International Concern (PHEIC). These trials have started much faster, are more geographically diverse, and are less likely to be funded by industry. However, the almost simultaneous design and initiation of hundreds of trials with 0.3 million participants across 78 countries creates the potential for congestion and inefficiencies and enhances risks for investors. Thus, an international coordination mechanism for clinical trials could be valuable in this and other situations.