A common language for Gulf War Illness (GWI) research studies: GWI common data elements.

AIMS: The Gulf War Illness programs (GWI) of the United States Department of Veteran Affairs and the Department of Defense Congressionally Directed Medical Research Program collaborated with experts to develop Common Data Elements (CDEs) to standardize and systematically collect, analyze, and share data across the (GWI) research community. MAIN METHODS: A collective working group of GWI advocates, Veterans, clinicians, and researchers convened to provide consensus on instruments, case report forms, and guidelines for GWI research. A similar initiative, supported by the National Institute of Neurologic Disorders and Stroke (NINDS) was completed for a comparative illness, Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS), and provided the foundation for this undertaking. The GWI working group divided into two sub-groups (symptoms and systems assessment). Both groups reviewed the applicability of instruments and forms recommended by the NINDS ME/CFS CDE to GWI research within specific domains and selected assessments of deployment exposures. The GWI CDE recommendations were finalized in March 2018 after soliciting public comments. KEY FINDINGS: GWI CDE recommendations are organized in 12 domains that include instruments, case report forms, and guidelines. Recommendations were categorized as core (essential), supplemental-highly recommended (essential for specified conditions, study types, or designs), supplemental (commonly collected, but not required), and exploratory (reasonable to use, but require further validation). Recommendations will continually be updated as GWI research progresses. SIGNIFICANCE: The GWI CDEs reflect the consensus recommendations of GWI research community stakeholders and will allow studies to standardize data collection, enhance data quality, and facilitate data sharing.

Therapeutic Immune Modulation against Solid Cancers with Intratumoral Poly-ICLC: A Pilot Trial.

Purpose: Polyinosinic-polycytidylic acid-poly-l-lysine carboxymethylcellulose (poly-ICLC), a synthetic double-stranded RNA complex, is a ligand for toll-like receptor-3 and MDA-5 that can activate immune cells, such as dendritic cells, and trigger natural killer cells to kill tumor cells.Patients and Methods: In this pilot study, eligible patients included those with recurrent metastatic disease in whom prior systemic therapy (head and neck squamous cell cancer and melanoma) failed. Patients received 2 treatment cycles, each cycle consisting of 1 mg poly-ICLC 3× weekly intratumorally (IT) for 2 weeks followed by intramuscular (IM) boosters biweekly for 7 weeks, with a 1-week rest period. Immune response was evaluated by immunohistochemistry (IHC) and RNA sequencing (RNA-seq) in tumor and blood.Results: Two patients completed 2 cycles of IT treatments, and 1 achieved clinical benefit (stable disease, progression-free survival 6 months), whereas the remainder had progressive disease. Poly-ICLC was well tolerated, with principal side effects of fatigue and inflammation at injection site (

A Predominant Oxidative Renal Metabolite of Empagliflozin in Male Mice Is Cytotoxic in Mouse Renal Tubular Cells but not Genotoxic.

In a previously reported CD-1 mouse 2-year carcinogenicity study with the sodium glucose cotransporter-2 inhibitor empagliflozin, an increased incidence of renal tubular adenomas and carcinomas was identified only in the male high-dose group. Follow-up investigative studies have shown that the renal tumors in male high-dose mice were preceded by a number of renal degenerative/regenerative findings. Prior cross-species in vitro metabolism studies using microsomes identified an oxidative metabolite (M466/2) predominantly formed in the male mouse kidney and which spontaneously degrades to a metabolite (M380/1) and reactive 4-OH crotonaldehyde (CTA). In order to further evaluate potential modes of action for empagliflozin-associated male mouse renal tumors, we report here a series of in vitro investigative toxicology studies conducted to evaluate the cytotoxic and genotoxic potential of empagliflozin and M466/2. To assess the cytotoxic potential of empagliflozin and M466/2, a primary mouse renal tubular epithelial (mRTE) cell model was used. In mRTE cells, M466/2-derived in vitro 4-OH CTA exposure was cytotoxic, while empagliflozin was not cytotoxic or mitogenic. Empagliflozin and M466/2 were not genotoxic, supporting an indirect mode of action for empagliflozin-associated male mouse renal tumorigenesis. In conclusion, these in vitro data show that M466/2-derived 4-OH CTA exposure is associated with cytotoxicity in renal tubule cells and may be involved in promoting compound-related in vivo renal metabolic stress and chronic low-level renal injury, in turn supporting a nongenotoxic mode of tumor pathogenesis specific to the male mouse.

Quantifying the complexity of medical research.

MOTIVATION: A crucial phenomenon of our times is the diminishing marginal returns of investments in pharmaceutical research and development. A potential reason is that research into diseases is becoming increasingly complex, and thus more burdensome, for humans to handle. We sought to investigate whether we could measure research complexity by analyzing the published literature. RESULTS: Through the text mining of the publication record of multiple diseases, we have found that the complexity and novelty of disease research has been increasing over the years. Surprisingly, we have also found that research on diseases with higher publication rate does not possess greater complexity or novelty than that on less-studied diseases. We have also shown that the research produced about a disease can be seen as a differentiated area of knowledge within the wider biomedical research. For our analysis, we have conceptualized disease research as a parallel multi-agent search in which each scientific agent (a scientist) follows a search path based on a model of a disease. We have looked at trends in facts published for diseases, measured their diversity and turnover using the entropy measure and found similar patterns across disease areas. CONTACT: raul.rodriguez-esteban@roche.com.

Analysis of information flows in interaction networks: implication for drug discovery and pharmacological research.

Frequent failures of experimental medicines in clinical trials question current concepts for predicting drug-effects in the human body. Improving the probability for success in drug discovery requires a better understanding of cause-effect relationships at the organism, organ, tissue, cellular, and molecular levels, each having a different degree of complexity. Despite the longstanding realization that clinical and preclinical drug-effect information needs to be integrated for generating more accurate forecasts of drug-effects, a road map for linking these disparate sources of information currently does not exist. This review focuses on a possible approach for obtaining these relationships by analyzing causes and effects on the basis of the topology of network interaction systems that process information at the cellular and organ system levels.

Cause-effect relationships in medicine: a protein network perspective.

Current target-based drug discovery platforms are not able to predict drug efficacy and the full spectrum of drug effects in organisms. Hence, many experimental drugs do not survive the lengthy and costly process of drug development. Understanding how drugs affect cellular network structures and how the resulting signals are translated into drug effects is extremely important for the discovery of new medicines. This requires a greater understanding of cause-effect relationships at the organism, organ, tissue, cellular, and molecular level. There is a growing recognition that this information must be integrated into discovery paradigms, but a 'road map' for obtaining and integrating information about heterogeneous networks into drug-discovery platforms currently does not exist. This review explores recent network-centered approaches developed to investigate the genesis of medicine and disease effects, specifically highlighting protein-protein interaction network models and their use in cause-effect analyses in medicine.

Drug effects viewed from a signal transduction network perspective.

Understanding how drugs affect cellular network structures and how resulting signals are translated into drug effects holds the key to the discovery of medicines. Herein we examine this cause-effect relationship by determining protein network structures associated with the generation of specific in vivo drug-effect patterns. Medicines having similar in vivo pharmacology have been identified by a comparison of drug-effect profiles of 1320 medicines. Protein network positions reached by these medicines were ascertained by examining the coinvestigation frequency of these medicines and 1179 protein network constituents in millions of scientific investigations. Interestingly, medicine associations obtained by comparing by drug-effect profiles mirror those obtained by comparing drug-protein coinvestigation frequency profiles, demonstrating that these drug-protein reachability profiles are relevant to in vivo pharmacology. By using protein associations obtained in these investigations and independent, curated protein interaction information, drug-mediated protein network topology models can be constructed. These protein network topology models reveal that drugs having similar pharmacology profiles reach similar discrete positions in cellular protein network systems and provide a network view of medicine cause-effect relationships.

Torcetrapib induces aldosterone and cortisol production by an intracellular calcium-mediated mechanism independently of cholesteryl ester transfer protein inhibition.

ILLUMINATE (Investigation of Lipid Level Management to Understand its Impact in Atherosclerotic Events), the phase 3 morbidity and mortality trial of torcetrapib, a cholesteryl ester transfer protein (CETP) inhibitor, identified previously undescribed changes in plasma levels of potassium, sodium, bicarbonate, and aldosterone. A key question after this trial is whether the failure of torcetrapib was a result of CETP inhibition or of some other pharmacology of the molecule. The direct effects of torcetrapib and related molecules on adrenal steroid production were assessed in cell culture using the H295R as well as the newly developed HAC15 human adrenal carcinoma cell lines. Torcetrapib induced the synthesis of both aldosterone and cortisol in these two in vitro cell systems. Analysis of steroidogenic gene expression indicated that torcetrapib significantly induced the expression of CYP11B2 and CYP11B1, two enzymes in the last step of aldosterone and cortisol biosynthesis pathway, respectively. Transcription profiling indicated that torcetrapib and angiotensin II share overlapping pathways in regulating adrenal steroid biosynthesis. Hormone-induced steroid production is mainly mediated by two messengers, calcium and cAMP. An increase of intracellular calcium was observed after torcetrapib treatment, whereas cAMP was unchanged. Consistent with intracellular calcium being the key mediator of torcetrapib's effect in adrenal cells, calcium channel blockers completely blocked torcetrapib-induced corticoid release and calcium increase. A series of compounds structurally related to torcetrapib as well as structurally distinct compounds were profiled. The results indicate that the pressor and adrenal effects observed with torcetrapib and related molecules are independent of CETP inhibition.

Analysis of system structure-function relationships.

Preclinical pharmacology studies conducted with experimental medicines currently focus on assessments of drug effects attributed to a drug's putative mechanism of action. The high failure rate of medicines in clinical trials, however, underscores that the information gathered from these studies is insufficient for forecasting drug effect profiles actually observed in patients. Improving drug effect predictions and increasing success rates of new medicines in clinical trials are some of the key challenges currently faced by the pharmaceutical industry. Addressing these challenges requires development of new methods for capturing and comparing "system-wide" structure-effect information for medicines at the cellular and organism levels. The current investigation describes a strategy for moving in this direction by using six different descriptor sets for examining the relationship between molecular structure and broad effect information of 1064 medicines at the cellular and the organism level. To compare broad drug effect information between different medicines, information spectra for each of the 1064 medicines were created, and the similarity between information spectra was determined through hierarchical clustering. The structure-effect relationships ascertained through these comparisons indicate that information spectra similarity obtained through preclinical ligand binding experiments using a model proteome provide useful estimates for the broad drug effect profiles of these 1064 medicines in organisms. This premise is illustrated using the ligand binding profiles of selected medicines in the dataset as biomarkers for forecasting system-wide effect observations of medicines that were not included in the incipient 1064-medicine analysis.

High-throughput electronic biology: mining information for drug discovery.

The vast range of in silico resources that are available in life sciences research hold much promise towards aiding the drug discovery process. To fully realize this opportunity, computational scientists must consider the practical issues of data integration and identify how best to apply these resources scientifically. In this article we describe in silico approaches that are driven towards the identification of testable laboratory hypotheses; we also address common challenges in the field. We focus on flexible, high-throughput techniques, which may be initiated independently of 'wet-lab' experimentation, and which may be applied to multiple disease areas. The utility of these approaches in drug discovery highlights the contribution that in silico techniques can make and emphasizes the need for collaboration between the areas of disease research and computational science.

Analysis of drug-induced effect patterns to link structure and side effects of medicines.

The high failure rate of experimental medicines in clinical trials accentuates inefficiencies of current drug discovery processes caused by a lack of tools for translating the information exchange between protein and organ system networks. Recently, we reported that biological activity spectra (biospectra), derived from in vitro protein binding assays, provide a mechanism for assessing a molecule's capacity to modulate the function of protein-network components. Herein we describe the translation of adverse effect data derived from 1,045 prescription drug labels into effect spectra and show their utility for diagnosing drug-induced effects of medicines. In addition, notwithstanding the limitation imposed by the quality of drug label information, we show that biospectrum analysis, in concert with effect spectrum analysis, provides an alignment between preclinical and clinical drug-induced effects. The identification of this alignment provides a mechanism for forecasting clinical effect profiles of medicines.

Biospectra analysis: model proteome characterizations for linking molecular structure and biological response.

Establishing quantitative relationships between molecular structure and broad biological effects has been a long-standing goal in drug discovery. Evaluation of the capacity of molecules to modulate protein functions is a prerequisite for understanding the relationship between molecular structure and in vivo biological response. A particular challenge in these investigations is to derive quantitative measurements of a molecule's functional activity pattern across different proteins. Herein we describe an operationally simple probabilistic structure-activity relationship (SAR) approach, termed biospectra analysis, for identifying agonist and antagonist effect profiles of medicinal agents by using pattern similarity between biological activity spectra (biospectra) of molecules as the determinant. Accordingly, in vitro binding data (percent inhibition values of molecules determined at single high drug concentration in a battery of assays representing a cross section of the proteome) are useful for identifying functional effect profile similarity between medicinal agents. To illustrate this finding, the relationship between biospectra similarity of 24 molecules, identified by hierarchical clustering of a 1567 molecule dataset as being most closely aligned with the neurotransmitter dopamine, and their agonist or antagonist properties was probed. Distinguishing the results described in this study from those obtained with affinity-based methods, the observed association between biospectra and biological response profile similarity remains intact even upon removal of putative drug targets from the dataset (four dopaminergic [D1/D2/D3/D4] and two adrenergic [alpha1 and alpha2] receptors). These findings indicate that biospectra analysis provides an unbiased new tool for forecasting structure-response relationships and for translating broad biological effect information into chemical structure design.

Biological spectra analysis: Linking biological activity profiles to molecular structure.

Establishing quantitative relationships between molecular structure and broad biological effects has been a longstanding challenge in science. Currently, no method exists for forecasting broad biological activity profiles of medicinal agents even within narrow boundaries of structurally similar molecules. Starting from the premise that biological activity results from the capacity of small organic molecules to modulate the activity of the proteome, we set out to investigate whether descriptor sets could be developed for measuring and quantifying this molecular property. Using a 1,567-compound database, we show that percent inhibition values, determined at single high drug concentration in a battery of in vitro assays representing a cross section of the proteome, provide precise molecular property descriptors that identify the structure of molecules. When broad biological activity of molecules is represented in spectra form, organic molecules can be sorted by quantifying differences between biological spectra. Unlike traditional structure-activity relationship methods, sorting of molecules by using biospectra comparisons does not require knowledge of a molecule's putative drug targets. To illustrate this finding, we selected as starting point the biological activity spectra of clotrimazole and tioconazole because their putative target, lanosterol demethylase (CYP51), was not included in the bioassay array. Spectra similarity obtained through profile similarity measurements and hierarchical clustering provided an unbiased means for establishing quantitative relationships between chemical structures and biological activity spectra. This methodology, which we have termed biological spectra analysis, provides the capability not only of sorting molecules on the basis of biospectra similarity but also of predicting simultaneous interactions of new molecules with multiple proteins.