Early Patient Stratification and Predictive Biomarkers in Drug Discovery and Development

Alkaptonuria: From Molecular Insights to a Dedicated Digital Platform

Alkaptonuria (AKU) is a genetic disorder that affects connective tissues of several body compartments causing cartilage degeneration, tendon calcification, heart problems, and an invalidating, early-onset form of osteoarthritis. The molecular mechanisms underlying AKU involve homogentisic acid (HGA) accumulation in cells and tissues. HGA is highly reactive, able to modify several macromolecules, and activates different pathways, mostly involved in the onset and propagation of oxidative stress and inflammation, with consequences spreading from the microscopic to the macroscopic level leading to irreversible damage. Gaining a deeper understanding of AKU molecular mechanisms may provide novel possible therapeutical approaches to counteract disease progression. In this review, we first describe inflammation and oxidative stress in AKU and discuss similarities with other more common disorders. Then, we focus on HGA reactivity and AKU molecular mechanisms. We finally describe a multi-purpose digital platform, named ApreciseKUre, created to facilitate data collection, integration, and analysis of AKU-related data.

Computational Approaches Integrated in a Digital Ecosystem Platform for a Rare Disease

Alkaptonuria (AKU) is an ultra-rare autosomal recessive disease caused by a mutation in the homogentisate 1,2-dioxygenase gene. One of the main obstacles in studying AKU and other ultra-rare diseases, is the lack of a standardized methodology to assess disease severity or response to treatment. Based on that, a multi-purpose digital platform, called ApreciseKUre, was implemented to facilitate data collection, integration and analysis for patients affected by AKU. It includes genetic, biochemical, histopathological, clinical, therapeutic resources and Quality of Life (QoL) scores that can be shared among registered researchers and clinicians to create a Precision Medicine Ecosystem. The combination of machine learning applications to analyse and re-interpret data available in the ApreciseKUre clearly indicated the potential direct benefits to achieve patients' stratification and the consequent tailoring of care and treatments to a specific subgroup of patients. In order to generate a comprehensive patient profile, computational modeling and database construction support the identification of potential new biomarkers, paving the way for more personalized therapy to maximize the benefit-risk ratio. In this work, different Machine Learning implemented approaches were described.

Network pharmacology: curing causal mechanisms instead of treating symptoms

For complex diseases, most drugs are highly ineffective, and the success rate of drug discovery is in constant decline. While low quality, reproducibility issues, and translational irrelevance of most basic and preclinical research have contributed to this, the current organ-centricity of medicine and the 'one disease-one target-one drug' dogma obstruct innovation in the most profound manner. Systems and network medicine and their therapeutic arm, network pharmacology, revolutionize how we define, diagnose, treat, and, ideally, cure diseases. Descriptive disease phenotypes are replaced by endotypes defined by causal, multitarget signaling modules that also explain respective comorbidities. Precise and effective therapeutic intervention is achieved by synergistic multicompound network pharmacology and drug repurposing, obviating the need for drug discovery and speeding up clinical translation.

Human disease biomarker panels through systems biology

As more uses for biomarkers are sought after for an increasing number of disease targets, single-target biomarkers are slowly giving way for biomarker panels. These panels incorporate various sources of biomolecular and clinical data to guarantee a higher robustness and power of separation for a clinical test. Multifactorial diseases such as psychiatric disorders show great potential for clinical use, assisting medical professionals during the analysis of risk and predisposition, disease diagnosis and prognosis, and treatment applicability and efficacy. More specific tests are also being developed to assist in ruling out, distinguishing between, and confirming suspicions of multifactorial diseases, as well as to predict which therapy option may be the best option for a given patient's biochemical profile. As more complex datasets are entering the field, involving multi-omic approaches, systems biology has stepped in to facilitate the discovery and validation steps during biomarker panel generation. Filtering biomolecules and clinical data, pre-validating and cross-validating potential biomarkers, generating final biomarker panels, and testing the robustness and applicability of those panels are all beginning to rely on machine learning and systems biology and research in this area will only benefit from advances in these approaches.

High-dimensionality Data Analysis of Pharmacological Systems Associated with Complex Diseases

It is widely accepted that molecular reductionist views of highly complex human physiologic activity, e.g., the aging process, as well as therapeutic drug efficacy are largely oversimplifications. Currently some of the most effective appreciation of biologic disease and drug response complexity is achieved using high-dimensionality (H-D) data streams from transcriptomic, proteomic, metabolomics, or epigenomic pipelines. Multiple H-D data sets are now common and freely accessible for complex diseases such as metabolic syndrome, cardiovascular disease, and neurodegenerative conditions such as Alzheimer's disease. Over the last decade our ability to interrogate these high-dimensionality data streams has been profoundly enhanced through the development and implementation of highly effective bioinformatic platforms. Employing these computational approaches to understand the complexity of age-related diseases provides a facile mechanism to then synergize this pathologic appreciation with a similar level of understanding of therapeutic-mediated signaling. For informative pathology and drug-based analytics that are able to generate meaningful therapeutic insight across diverse data streams, novel informatics processes such as latent semantic indexing and topological data analyses will likely be important. Elucidation of H-D molecular disease signatures from diverse data streams will likely generate and refine new therapeutic strategies that will be designed with a cognizance of a realistic appreciation of the complexity of human age-related disease and drug effects. We contend that informatic platforms should be synergistic with more advanced chemical/drug and phenotypic cellular/tissue-based analytical predictive models to assist in either de novo drug prioritization or effective repurposing for the intervention of aging-related diseases. SIGNIFICANCE STATEMENT: All diseases, as well as pharmacological mechanisms, are far more complex than previously thought a decade ago. With the advent of commonplace access to technologies that produce large volumes of high-dimensionality data (e.g., transcriptomics, proteomics, metabolomics), it is now imperative that effective tools to appreciate this highly nuanced data are developed. Being able to appreciate the subtleties of high-dimensionality data will allow molecular pharmacologists to develop the most effective multidimensional therapeutics with effectively engineered efficacy profiles.

Network and Systems Medicine: Position Paper of the European Collaboration on Science and Technology Action on Open Multiscale Systems Medicine

Introduction: Network and systems medicine has rapidly evolved over the past decade, thanks to computational and integrative tools, which stem in part from systems biology. However, major challenges and hurdles are still present regarding validation and translation into clinical application and decision making for precision medicine. Methods: In this context, the Collaboration on Science and Technology Action on Open Multiscale Systems Medicine (OpenMultiMed) reviewed the available advanced technologies for multidimensional data generation and integration in an open-science approach as well as key clinical applications of network and systems medicine and the main issues and opportunities for the future. Results: The development of multi-omic approaches as well as new digital tools provides a unique opportunity to explore complex biological systems and networks at different scales. Moreover, the application of findable, applicable, interoperable, and reusable principles and the adoption of standards increases data availability and sharing for multiscale integration and interpretation. These innovations have led to the first clinical applications of network and systems medicine, particularly in the field of personalized therapy and drug dosing. Enlarging network and systems medicine application would now imply to increase patient engagement and health care providers as well as to educate the novel generations of medical doctors and biomedical researchers to shift the current organ- and symptom-based medical concepts toward network- and systems-based ones for more precise diagnoses, interventions, and ideally prevention. Conclusion: In this dynamic setting, the health care system will also have to evolve, if not revolutionize, in terms of organization and management.

Experimental verification of regression to the mean in redox biology: differential responses to exercise

An important methodological threat when selecting individuals based on initial values for a given trait is the "regression to the mean" artifact. This artifact appears when a group with an extreme mean value during a first measurement tends to obtain a less extreme value (i.e. tends toward the mean) on a subsequent measurement. The main aim was to experimentally confirm the presence of this artifact in the responses of the reference oxidative stress biomarker (F₂-isoprostanes) after exercise. Urine samples were collected before and immediately following acute exercise in order to determine the level of exercise-induced oxidative stress. Afterwards, participants were arranged into three groups based on their levels of exercise-induced oxidative stress (low, moderate and high oxidative stress groups; n = 12 per group). In order to verify the existence of the regression to the mean artifact, the three groups were subjected to a second exercise trial one week after the first trial. This study confirmed the regression to the mean artifact in a redox biology context and showed that this artifact can be minimized by performing a duplicate pretreatment measurement after completing a nonrandom sorting based on the first assessment. This study also indicated that different individuals experience high oxidative stress or reductive stress (or no stress) to the same exercise stimulus even after adjusting for regression to the mean. This finding substantiates the methodological choice to divide individuals based on their degree of exercise-induced oxidative stress in future experiments to investigate the role of reactive species in exercise adaptations.

Regulatory Forum Opinion Piece*: Veterinary Pathologists in Translational Pharmacology and Biomarker Integration in Drug Discovery and Development

This article highlights emerging roles for veterinary pathologists outside of traditional functions and in line with the translational research (TR) approach. Veterinary pathologists offer unique and valuable expertise toward addressing particular TR and associated translational pharmacology questions, identifying gaps and risks in biomarker and pathology strategies, and advancing TR team decision making. Veterinary pathologists' attributes that are integral to the TR approach include (i) well-developed understanding of comparative physiology, pathology, and disease; (ii) extensive experience in interpretation and integration of complex data sets on whole-body responses and utilizing this for deciphering pathogenesis and translating events between laboratory species and man; (iii) proficiency in recognizing differences in disease end points among individuals, animal species and strains, and assessing correlations between these differences and other investigative (including biomarker) findings; and (iv) strong background in a wide spectrum of research technologies that can address pathomechanistic questions and biomarker needs. Some of the more evident roles in which veterinary pathologists can offer their greatest contributions to address questions and strategies of TR and biomarker integration will be emphasized.

Bioinformatics Mining and Modeling Methods for the Identification of Disease Mechanisms in Neurodegenerative Disorders

Since the decoding of the Human Genome, techniques from bioinformatics, statistics, and machine learning have been instrumental in uncovering patterns in increasing amounts and types of different data produced by technical profiling technologies applied to clinical samples, animal models, and cellular systems. Yet, progress on unravelling biological mechanisms, causally driving diseases, has been limited, in part due to the inherent complexity of biological systems. Whereas we have witnessed progress in the areas of cancer, cardiovascular and metabolic diseases, the area of neurodegenerative diseases has proved to be very challenging. This is in part because the aetiology of neurodegenerative diseases such as Alzheimer´s disease or Parkinson´s disease is unknown, rendering it very difficult to discern early causal events. Here we describe a panel of bioinformatics and modeling approaches that have recently been developed to identify candidate mechanisms of neurodegenerative diseases based on publicly available data and knowledge. We identify two complementary strategies-data mining techniques using genetic data as a starting point to be further enriched using other data-types, or alternatively to encode prior knowledge about disease mechanisms in a model based framework supporting reasoning and enrichment analysis. Our review illustrates the challenges entailed in integrating heterogeneous, multiscale and multimodal information in the area of neurology in general and neurodegeneration in particular. We conclude, that progress would be accelerated by increasing efforts on performing systematic collection of multiple data-types over time from each individual suffering from neurodegenerative disease. The work presented here has been driven by project AETIONOMY; a project funded in the course of the Innovative Medicines Initiative (IMI); which is a public-private partnership of the European Federation of Pharmaceutical Industry Associations (EFPIA) and the European Commission (EC).

Computational Modelling Approaches on Epigenetic Factors in Neurodegenerative and Autoimmune Diseases and Their Mechanistic Analysis

Neurodegenerative as well as autoimmune diseases have unclear aetiologies, but an increasing number of evidences report for a combination of genetic and epigenetic alterations that predispose for the development of disease. This review examines the major milestones in epigenetics research in the context of diseases and various computational approaches developed in the last decades to unravel new epigenetic modifications. However, there are limited studies that systematically link genetic and epigenetic alterations of DNA to the aetiology of diseases. In this work, we demonstrate how disease-related epigenetic knowledge can be systematically captured and integrated with heterogeneous information into a functional context using Biological Expression Language (BEL). This novel methodology, based on BEL, enables us to integrate epigenetic modifications such as DNA methylation or acetylation of histones into a specific disease network. As an example, we depict the integration of epigenetic and genetic factors in a functional context specific to Parkinson's disease (PD) and Multiple Sclerosis (MS).

Developing biomarkers in mood disorders research through the use of rapid-acting antidepressants

An impediment to progress in mood disorders research is the lack of analytically valid and qualified diagnostic and treatment biomarkers. Consistent with the National Institute of Mental Health (NIMH)'s Research Domain Criteria (RDoC) initiative, the lack of diagnostic biomarkers has precluded us from moving away from a purely subjective (symptom-based) toward a more objective diagnostic system. In addition, treatment response biomarkers in mood disorders would facilitate drug development and move beyond trial-and-error toward more personalized treatments. As such, biomarkers identified early in the pathophysiological process are proximal biomarkers (target engagement), while those occurring later in the disease process are distal (disease pathway components). One strategy to achieve this goal in biomarker development is to increase efforts at the initial phases of biomarker development (i.e. exploration and validation) at single sites with the capability of integrating multimodal approaches across a biological systems level. Subsequently, resultant putative biomarkers could then undergo characterization and surrogacy as these latter phases require multisite collaborative efforts. We have used multimodal approaches - genetics, proteomics/metabolomics, peripheral measures, multimodal neuroimaging, neuropsychopharmacological challenge paradigms and clinical predictors - to explore potential predictor and mediator/moderator biomarkers of the rapid-acting antidepressants ketamine and scopolamine. These exploratory biomarkers may then be used for a priori stratification in larger multisite controlled studies during the validation and characterization phases with the ultimate goal of surrogacy. In sum, the combination of target engagement and well-qualified disease-related measures are crucial to improve our pathophysiological understanding, personalize treatment selection, and expand our armamentarium of novel therapeutics.

Recent advances in modeling languages for pathway maps and computable biological networks

As our theories of systems biology grow more sophisticated, the models we use to represent them become larger and more complex. Languages necessarily have the expressivity and flexibility required to represent these models in ways that support high-resolution annotation, and provide for simulation and analysis that are sophisticated enough to allow researchers to master their data in the proper context. These languages also need to facilitate model sharing and collaboration, which is currently best done by using uniform data structures (such as graphs) and language standards. In this brief review, we discuss three of the most recent systems biology modeling languages to appear: BEL, PySB and BCML, and examine how they meet these needs.

Systems diagnostics: anticipating the next generation of diagnostic tests based on mechanistic insight into disease

Societal demand for faster and more accurate assignment of treatments is based in both patient care needs and in health economics. From a patient care standpoint, there needs to be a transformation from the empiric method of therapeutic decision making to avoid unwanted side effects from inefficacious treatments. For health economics, the delay in effective therapy and expenditures for ineffective therapies add to the burden of care. To accomplish this transformation, we need to modify our current method of classifying disease from a phenotypic description to one that incorporates the different molecular drivers that created the observed phenotype. To do so, a deeper, systems-based understanding of these disease drivers is required, which will yield a new generation of diagnostic tests, or systems diagnostics.

Company Profile: Selventa, Inc

Selventa, Inc. (MA, USA) is a biomarker discovery company that enables personalized healthcare. Originally founded as Genstruct, Inc., Selventa has undergone significant evolution from a technology-based service provider to an active partner in the development of diagnostic tests, functioning as a molecular dashboard of disease activity using a unique platform. As part of that evolution, approximately 2 years ago the company was rebranded as Selventa to reflect its new identity and mission. The contributions to biomedical research by Selventa are based on in silico, reverse-engineering methods to determine biological causality. That is, given a set of in vitro or in vivo biological observations, which biological mechanisms can explain the measured results? Facilitated by a large and carefully curated knowledge base, these in silico methods generated new insights into the mechanisms driving a disease. As Selventa's methods would enable biomarker discovery and be directly applicable to generating novel diagnostics, the scientists at Selventa have focused on the development of predictive biomarkers of response in autoimmune and oncologic diseases. Selventa is presently building a portfolio of independent, as well as partnered, biomarker projects with the intention to create diagnostic tests that predict response to therapy.