Tribulations and future opportunities for artificial intelligence in precision medicine.

Upon a diagnosis, the clinical team faces two main questions: what treatment, and at what dose? Clinical trials' results provide the basis for guidance and support for official protocols that clinicians use to base their decisions. However, individuals do not consistently demonstrate the reported response from relevant clinical trials. The decision complexity increases with combination treatments where drugs administered together can interact with each other, which is often the case. Additionally, the individual's response to the treatment varies with the changes in their condition. In practice, the drug and the dose selection depend significantly on the medical protocol and the medical team's experience. As such, the results are inherently varied and often suboptimal. Big data and Artificial Intelligence (AI) approaches have emerged as excellent decision-making tools, but multiple challenges limit their application. AI is a rapidly evolving and dynamic field with the potential to revolutionize various aspects of human life. AI has become increasingly crucial in drug discovery and development. AI enhances decision-making across different disciplines, such as medicinal chemistry, molecular and cell biology, pharmacology, pathology, and clinical practice. In addition to these, AI contributes to patient population selection and stratification. The need for AI in healthcare is evident as it aids in enhancing data accuracy and ensuring the quality care necessary for effective patient treatment. AI is pivotal in improving success rates in clinical practice. The increasing significance of AI in drug discovery, development, and clinical trials is underscored by many scientific publications. Despite the numerous advantages of AI, such as enhancing and advancing Precision Medicine (PM) and remote patient monitoring, unlocking its full potential in healthcare requires addressing fundamental concerns. These concerns include data quality, the lack of well-annotated large datasets, data privacy and safety issues, biases in AI algorithms, legal and ethical challenges, and obstacles related to cost and implementation. Nevertheless, integrating AI in clinical medicine will improve diagnostic accuracy and treatment outcomes, contribute to more efficient healthcare delivery, reduce costs, and facilitate better patient experiences, making healthcare more sustainable. This article reviews AI applications in drug development and clinical practice, making healthcare more sustainable, and highlights concerns and limitations in applying AI.

Insights and Strategies of Melanoma Immunotherapy: Predictive Biomarkers of Response and Resistance and Strategies to Improve Response Rates.

Despite the recent successes and durable responses with immune checkpoint inhibitors (ICI), many cancer patients, including those with melanoma, do not derive long-term benefits from ICI therapies. The lack of predictive biomarkers to stratify patients to targeted treatments has been the driver of primary treatment failure and represents an unmet medical need in melanoma and other cancers. Understanding genomic correlations with response and resistance to ICI will enhance cancer patients' benefits. Building on insights into interplay with the complex tumor microenvironment (TME), the ultimate goal should be assessing how the tumor 'instructs' the local immune system to create its privileged niche with a focus on genomic reprogramming within the TME. It is hypothesized that this genomic reprogramming determines the response to ICI. Furthermore, emerging genomic signatures of ICI response, including those related to neoantigens, antigen presentation, DNA repair, and oncogenic pathways, are gaining momentum. In addition, emerging data suggest a role for checkpoint regulators, T cell functionality, chromatin modifiers, and copy-number alterations in mediating the selective response to ICI. As such, efforts to contextualize genomic correlations with response into a more insightful understanding of tumor immune biology will help the development of novel biomarkers and therapeutic strategies to overcome ICI resistance.

Catchment-wide validated assessment of combined sewer overflows (CSOs) in a mediterranean coastal area and possible disinfection methods to mitigate microbial contamination.

The first phase of this study aimed to evaluate the environmental impact of combined sewer overflow (CSO) events originated from 35 spillways on the Rio Vallescura catchment (Central Italy) and to understand their contribution to the deterioration of the coastal bathing water quality. A specific analytical campaign was carried out in the sewer system and a dynamic rainfall-runoff simulation model was developed and integrated with a water quality model and further validated. The simulations led to identify the most critical spills in terms of flow rate and selected pollutant loads (i.e. suspended solids, biochemical oxygen demand, chemical oxygen demand, total Kjeldahl nitrogen, Escherichia coli). Specifically, the E. coli release in the water body due to CSO events represented almost 100% of the different pollutant sources considered. In the second phase, the applicability of various disinfection methods was investigated on the CSOs introduced into the catchment. On site physical (UV) and lab-scale chemical (peracetic acid (PAA), performic acid (PFA), ozone) disinfectant agents were tested on microbial indicators including E. coli and intestinal enterococci. PFA and ozone were more effective on the removal of both bacteria (above 3.5 log units) even at low concentration and with short contact time; whereas, PAA showed a moderate removal efficiency (around 2.5 log units) only for E. coli. The highest removal efficiency was achieved in the on-site UV unit and none of the indicator bacteria was detected in the final effluent after the sand filtration and UV treatment. Finally, potential scenarios were developed in comparison to the baseline scenario for the management and treatment of CSOs where a mitigation of E. coli loads from 28% to 73% was achieved on the receiving water body, and a comparative cost assessment of the disinfection methods was provided for in situ treatment of the most critical spillway.

Are innovation and new technologies in precision medicine paving a new era in patients centric care?

Healthcare is undergoing a transformation, and it is imperative to leverage new technologies to generate new data and support the advent of precision medicine (PM). Recent scientific breakthroughs and technological advancements have improved our understanding of disease pathogenesis and changed the way we diagnose and treat disease leading to more precise, predictable and powerful health care that is customized for the individual patient. Genetic, genomics, and epigenetic alterations appear to be contributing to different diseases. Deep clinical phenotyping, combined with advanced molecular phenotypic profiling, enables the construction of causal network models in which a genomic region is proposed to influence the levels of transcripts, proteins, and metabolites. Phenotypic analysis bears great importance to elucidat the pathophysiology of networks at the molecular and cellular level. Digital biomarkers (BMs) can have several applications beyond clinical trials in diagnostics-to identify patients affected by a disease or to guide treatment. Digital BMs present a big opportunity to measure clinical endpoints in a remote, objective and unbiased manner. However, the use of "omics" technologies and large sample sizes have generated massive amounts of data sets, and their analyses have become a major bottleneck requiring sophisticated computational and statistical methods. With the wealth of information for different diseases and its link to intrinsic biology, the challenge is now to turn the multi-parametric taxonomic classification of a disease into better clinical decision-making by more precisely defining a disease. As a result, the big data revolution has provided an opportunity to apply artificial intelligence (AI) and machine learning algorithms to this vast data set. The advancements in digital health opportunities have also arisen numerous questions and concerns on the future of healthcare practices in particular with what regards the reliability of AI diagnostic tools, the impact on clinical practice and vulnerability of algorithms. AI, machine learning algorithms, computational biology, and digital BMs will offer an opportunity to translate new data into actionable information thus, allowing earlier diagnosis and precise treatment options. A better understanding and cohesiveness of the different components of the knowledge network is a must to fully exploit the potential of it.

Chromosome conformation signatures define predictive markers of inadequate response to methotrexate in early rheumatoid arthritis.

BACKGROUND: There is a pressing need in rheumatoid arthritis (RA) to identify patients who will not respond to first-line disease-modifying anti-rheumatic drugs (DMARD). We explored whether differences in genomic architecture represented by a chromosome conformation signature (CCS) in blood taken from early RA patients before methotrexate (MTX) treatment could assist in identifying non-response to DMARD and, whether there is an association between such a signature and RA specific expression quantitative trait loci (eQTL). METHODS: We looked for the presence of a CCS in blood from early RA patients commencing MTX using chromosome conformation capture by EpiSwitch™. Using blood samples from MTX responders, non-responders and healthy controls, a custom designed biomarker discovery array was refined to a 5-marker CCS that could discriminate between responders and non-responders to MTX. We cross-validated the predictive power of the CCS by generating 150 randomized groups of 59 early RA patients (30 responders and 29 non-responders) before MTX treatment. The CCS was validated using a blinded, independent cohort of 19 early RA patients (9 responders and 10 non-responders). Last, the loci of the CCS markers were mapped to RA-specific eQTL. RESULTS: We identified a 5-marker CCS that could identify, at baseline, responders and non-responders to MTX. The CCS consisted of binary chromosome conformations in the genomic regions of IFNAR1, IL-21R, IL-23, CXCL13 and IL-17A. When tested on a cohort of 59 RA patients, the CCS provided a negative predictive value of 90.0% for MTX response. When tested on a blinded independent validation cohort of 19 early RA patients, the signature demonstrated a true negative response rate of 86 and a 90% sensitivity for detection of non-responders to MTX. Only conformations in responders mapped to RA-specific eQTL. CONCLUSIONS: Here we demonstrate that detection of a CCS in blood in early RA is able to predict inadequate response to MTX with a high degree of accuracy. Our results provide a proof of principle that a priori stratification of response to MTX is possible, offering a mechanism to provide alternative treatments for non-responders to MTX earlier in the course of the disease.

Corrigendum: The RA-MAP Consortium: a working model for academia-industry collaboration.

This corrects the article DOI: 10.1038/nrrheum.2017.200.

The RA-MAP Consortium: a working model for academia-industry collaboration.

Collaboration can be challenging; nevertheless, the emerging successes of large, multi-partner, multi-national cooperatives and research networks in the biomedical sector have sustained the appetite of academics and industry partners for developing and fostering new research consortia. This model has percolated down to national funding agencies across the globe, leading to funding for projects that aim to realise the true potential of genomic medicine in the 21st century and to reap the rewards of 'big data'. In this Perspectives article, the experiences of the RA-MAP consortium, a group of more than 140 individuals affiliated with 21 academic and industry organizations that are focused on making genomic medicine in rheumatoid arthritis a reality are described. The challenges of multi-partner collaboration in the UK are highlighted and wide-ranging solutions are offered that might benefit large research consortia around the world.

Assessing the Immunogenicity of Biopharmaceuticals.

Biopharmaceuticals have the potential to raise an immunogenic response in treated individuals, which may impact the efficacy and safety profile of these drugs. As a result, it is essential to evaluate immunogenicity throughout the different phases of the clinical development of a biopharmaceutical, including post-marketing surveillance. Although rigorous evaluation of biopharmaceutical immunogenicity is required by regulatory authorities, there is a lack of uniform standards for the type, quantity, and quality of evidence, and for guidance on experimental design for immunogenicity assays or criteria to compare immunogenicity of biopharmaceuticals. Moreover, substantial technological advances in methods to assess immune responses have yielded higher immunogenicity rates with modern assays, and limit comparison of immunogenicity of biopharmaceuticals outside of head-to-head clinical trials. Accordingly, research programs, regulatory agencies, and clinicians need to keep pace with continuously evolving analyses of immunogenicity. Here, we review factors associated with immunogenicity of biopharmaceuticals, potential clinical ramifications, and current regulatory guidance for evaluating immunogenicity, and discuss methods to assess immunogenicity in non-clinical and clinical studies. We also describe special considerations for evaluating the immunogenicity of biosimilar candidates.

Biomarkers: a valuable tool in clinical research and medical practice.

The importance of biomarker technology and biomarker strategies in pharmaceutical development is still in its infancy, but the impact of biomarkers is already proving to be significant in this field. Strategies for incorporating biomarkers form the basis for translational medicine and also for the industry/regulatory focus on reducing the high attrition rate of drugs often encountered at phase II clinical research. The depth and breadth of knowledge required to successfully implement biomarkers into drug development are generating many collaborative efforts within the pharmaceutical industry, as well as encouraging the involvement of professionals who traditionally have not been part of the drug-development process.