Effects of vitamin D signaling in cardiovascular disease: centrality of macrophage polarization.

Among the leading causes of natural death are cardiovascular diseases, cancer, and respiratory diseases. Factors causing illness include genetic predisposition, aging, stress, chronic inflammation, environmental factors, declining autophagy, and endocrine abnormalities including insufficient vitamin D levels. Inconclusive clinical outcomes of vitamin D supplements in cardiovascular diseases demonstrate the need to identify cause-effect relationships without bias. We employed a spectral clustering methodology capable of analyzing large diverse datasets for examining the role of vitamin D's genomic and non-genomic signaling in disease in this study. The results of this investigation showed the following: (1) vitamin D regulates multiple reciprocal feedback loops including p53, macrophage autophagy, nitric oxide, and redox-signaling; (2) these regulatory schemes are involved in over 2,000 diseases. Furthermore, the balance between genomic and non-genomic signaling by vitamin D affects autophagy regulation of macrophage polarization in tissue homeostasis. These findings provide a deeper understanding of how interactions between genomic and non-genomic signaling affect vitamin D pharmacology and offer opportunities for increasing the efficacy of vitamin D-centered treatment of cardiovascular disease and healthy lifespans.

Autophagy: An Emerging Target for Developing Effective Analgesics.

Inadequate treatment of acute and chronic pain causes depression, anxiety, sleep disturbances, and increased mortality. Abuse and overdose of opioids and the side effects associated with chronic use of NSAID illustrate the need for development of safer and effective pain medication. Working toward this end, an in silico tool based on an emergent intelligence analytical platform that examines interactions between protein networks was used to identify molecular mechanisms involved in regulating the body's response to painful stimuli and drug treatments. Examining interactions between protein networks associated with the expression of over 20 different pain types suggests that the regulation of autophagy plays a central role in modulation of pain symptoms (see Materials and Methods). Using the topology of this regulatory scheme as an in silico screening tool, we identified that combinations of functions targeted by cannabidiol, myo-inositol, and fish oils with varying ratios of eicosapentaenoic and docosahexaenoic acids are projected to produce superior analgesia. For validating this prediction, we administered combinations of cannabidiol, myo-inositol, and fish oils to rats that received formalin injections in hind paws, prior to substance administration, and showed that analgesic effects produced by these combinations were comparable or superior to known NSAID analgesics, which suggests that these combinations have potential in treatment of pain.

Functional characterization of nutraceuticals using spectral clustering: Centrality of caveolae-mediated endocytosis for management of nitric oxide and vitamin D deficiencies and atherosclerosis.

It is well recognized that redox imbalance, nitric oxide (NO), and vitamin D deficiencies increase risk of cardiovascular, metabolic, and infectious diseases. However, clinical studies assessing efficacy of NO and vitamin D supplementation have failed to produce unambiguous efficacy outcomes suggesting that the understanding of the pharmacologies involved is incomplete. This raises the need for using systems pharmacology tools to better understand cause-effect relationships at biological systems levels. We describe the use of spectral clustering methodology to analyze protein network interactions affected by a complex nutraceutical, Cardio Miracle (CM), that contains arginine, citrulline, vitamin D, and antioxidants. This examination revealed that interactions between protein networks affected by these substances modulate functions of a network of protein complexes regulating caveolae-mediated endocytosis (CME), TGF beta activity, vitamin D efficacy and host defense systems. Identification of this regulatory scheme and the working of embedded reciprocal feedback loops has significant implications for treatment of vitamin D deficiencies, atherosclerosis, metabolic and infectious diseases such as COVID-19.

Discovery and characterization of a novel dihydroisoxazole class of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor potentiators.

Positive allosteric modulators ("potentiators") of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors (AMPAR) enhance excitatory neurotransmission and may improve the cognitive deficits associated with various neurological disorders. The dihydroisoxazole (DHI) series of AMPAR potentiators described herein originated from the identification of 7 by a high-throughput functional activity screen using mouse embryonic stem (mES) cell-derived neuronal precursors. Subsequent structure-based drug design using X-ray crystal structures of the ligand-binding domain of human GluA2 led to the discovery of both PF-04725379 (11), which in tritiated form became a novel ligand for characterizing the binding affinities of subsequent AMPAR potentiators in rat brain homogenate, and PF-04701475 (8a), a prototype used to explore AMPAR-mediated pharmacology in vivo. Lead series optimization provided 16a, a functionally potent compound lacking the potentially bioactivatable aniline within 8a, but retaining desirable in vitro ADME properties.

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.

Quantitative structure-activity relationship of phenoxyphenyl-methanamine compounds with 5HT2A, SERT, and hERG activities.

QSAR models have been used to evaluate activities for compounds in the phenoxyphenyl-methanamine (PPMA) class of compounds. These models utilize Hammett-type donating-withdrawing substituent values as well as simple parameters to describe substituent size and elucidate the SAR of the 'A' and 'B' rings. Using this methodology, intuitive QSAR relationships were found for the three biological activities with R(2) values of 0.73, 0.45, and 0.58 for 5HT(2A), SerT, and hERG activities.

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.

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.