Mapping the effects of drugs on the immune system

Factors Affecting SARS-CoV-2 IgG Production after Vaccination and/or Disease: A Large-Scale Seroprevalence Study

This study aimed at identifying factors influencing SARS-CoV-2-specific IgG antibody levels after vaccination and/or infection. Between January 2022 and March 2023, 2000 adults (≥18 years, Salzburg, Austria) participated in this population-based seroprevalence study by providing 3 mL of blood to detect SARS-CoV-2-specific IgG antibodies using an anti-SARS-CoV-2 IgG quantitative assay and by completing a self-designed questionnaire including anthropometric factors, vaccination information, and medical history. For 77 of the participants, a time-course study up to 24 weeks post vaccination or quarantine end was performed. Convalescent-only subjects had the lowest median antibody titer (65.6 BAU/mL) compared to vaccinated and hybrid immunized subjects (p-value < 0.0001) The type of vaccine as well as vaccine combinations significantly influenced the levels of SARS-CoV-2 spike-protein-specific IgG, ranging from a median antibody level of 770.5 BAU/mL in subjects who were vaccinated only to 3020.0 BAU/mL in hybrid immunized subjects (p-value < 0.0001). Over time, a significant decline in the levels of neutralizing antibodies was found. Depending on the subpopulation analyzed, further significant influencing factors included sex assigned at birth, disease severity, chronic diseases, and medication. A hybrid immunization resulted in more robust immune responses. Nevertheless, there were multiple other factors impacting these responses. This knowledge should be included in future vaccination strategies and serve as a guide in the development of personalized medicine.

COIMMR: a computational framework to reveal the contribution of herbal ingredients against human cancer via immune microenvironment and metabolic reprogramming

Immune evasion and metabolism reprogramming have been regarded as two vital hallmarks of the mechanism of carcinogenesis. Thus, targeting the immune microenvironment and the reprogrammed metabolic processes will aid in developing novel anti-cancer drugs. In recent decades, herbal medicine has been widely utilized to treat cancer through the modulation of the immune microenvironment and reprogrammed metabolic processes. However, labor-based herbal ingredient screening is time consuming, laborious and costly. Luckily, some computational approaches have been proposed to screen candidates for drug discovery rapidly. Yet, it has been challenging to develop methods to screen drug candidates exclusively targeting specific pathways, especially for herbal ingredients which exert anti-cancer effects by multiple targets, multiple pathways and synergistic ways. Meanwhile, currently employed approaches cannot quantify the contribution of the specific pathway to the overall curative effect of herbal ingredients. Hence, to address this problem, this study proposes a new computational framework to infer the contribution of the immune microenvironment and metabolic reprogramming (COIMMR) in herbal ingredients against human cancer and specifically screen herbal ingredients targeting the immune microenvironment and metabolic reprogramming. Finally, COIMMR was applied to identify isoliquiritigenin that specifically regulates the T cells in stomach adenocarcinoma and cephaelin hydrochloride that specifically targets metabolic reprogramming in low-grade glioma. The in silico results were further verified using in vitro experiments. Taken together, our approach opens new possibilities for repositioning drugs targeting immune and metabolic dysfunction in human cancer and provides new insights for drug development in other diseases. COIMMR is available at https://github.com/LYN2323/COIMMR.

Sensor Array-Enabled Identification of Drugs for Repolarization of Macrophages to Anti-Inflammatory Phenotypes

Macrophages are key components of the innate immune system that have essential functions in physiological processes and diseases. The phenotypic plasticity of macrophages allows cells to be polarized into a multidimensional spectrum of phenotypes, broadly classed as pro-inflammatory (M1) and anti-inflammatory (M2) states. Repolarization of M1 to M2 phenotypes alters the immune response to ameliorate autoimmune and inflammation-associated diseases. Detection of this repolarization, however, is challenging to execute in high-throughput applications. In this work, we demonstrate the ability of a single polymer fabricated to provide a six-channel sensor array that can determine macrophage polarization phenotypes. This sensing platform provides a sensitive and high-throughput tool for detecting drug-induced M1-to-M2 repolarization, allowing the identification of new therapeutic leads for inflammatory diseases. The ability of this sensor array to discriminate different M2 subtypes induced by drugs can also improve the efficacy evaluation of anti-inflammatory drugs and avoid adverse effects.

A T Cell-Engaging Tumor Organoid Platform for Pancreatic Cancer Immunotherapy

Pancreatic ductal adenocarcinoma (PDA) is a clinically challenging disease with limited treatment options. Despite a small percentage of cases with defective mismatch DNA repair (dMMR), PDA is included in the most immune-resistant cancer types that are poorly responsive to immune checkpoint blockade (ICB) therapy. To facilitate drug discovery combating this immunosuppressive tumor type, a high-throughput drug screen platform is established with the newly developed T cell-incorporated pancreatic tumor organoid model. Tumor-specific T cells are included in the pancreatic tumor organoids by two-step cell packaging, fully recapitulating immune infiltration in the immunosuppressive tumor microenvironment (TME). The organoids are generated with key components in the original tumor, including epithelial, vascular endothelial, fibroblast and macrophage cells, and then packaged with T cells into their outside layer mimicking a physical barrier and enabling T cell infiltration and cytotoxicity studies. In the PDA organoid-based screen, epigenetic inhibitors ITF2357 and I-BET151 are identified, which in combination with anti-PD-1 based therapy show considerably greater anti-tumor effect. The combinatorial treatment turns the TME from immunosuppressive to immunoactive, up-regulates the MHC-I antigen processing and presentation, and enhances the effector T cell activity. The standardized PDA organoid model has shown great promise to accelerate drug discovery for the immunosuppressive cancer.

A pilot metabolomic study of drug interaction with the immune response to seasonal influenza vaccination

Many human diseases, including metabolic diseases, are intertwined with the immune system. The understanding of how the human immune system interacts with pharmaceutical drugs is still limited, and epidemiological studies only start to emerge. As the metabolomics technology matures, both drug metabolites and biological responses can be measured in the same global profiling data. Therefore, a new opportunity presents itself to study the interactions between pharmaceutical drugs and immune system in the high-resolution mass spectrometry data. We report here a double-blinded pilot study of seasonal influenza vaccination, where half of the participants received daily metformin administration. Global metabolomics was measured in the plasma samples at six timepoints. Metformin signatures were successfully identified in the metabolomics data. Statistically significant metabolite features were found both for the vaccination effect and for the drug-vaccine interactions. This study demonstrates the concept of using metabolomics to investigate drug interaction with the immune response in human samples directly at molecular levels.

Multi-omic molecular profiling and network biology for precision anaesthesiology: a narrative review

Technological advancement, data democratisation, and decreasing costs have led to a revolution in molecular biology in which the entire set of DNA, RNA, proteins, and various other molecules - the 'multi-omic' profile - can be measured in humans. Sequencing 1 million bases of human DNA now costs US$0.01, and emerging technologies soon promise to reduce the cost of sequencing the whole genome to US$100. These trends have made it feasible to sample the multi-omic profile of millions of people, much of which is publicly available for medical research. Can anaesthesiologists use these data to improve patient care? This narrative review brings together a rapidly growing literature in multi-omic profiling across numerous fields that points to the future of precision anaesthesiology. Here, we discuss how DNA, RNA, proteins, and other molecules interact in molecular networks that can be used for preoperative risk stratification, intraoperative optimisation, and postoperative monitoring. This literature provides evidence for four fundamental insights: (1) Clinically similar patients have different molecular profiles and, as a consequence, different outcomes. (2) Vast, publicly available, and rapidly growing molecular datasets have been generated in chronic disease patients and can be repurposed to estimate perioperative risk. (3) Multi-omic networks are altered in the perioperative period and influence postoperative outcomes. (4) Multi-omic networks can serve as empirical, molecular measurements of a successful postoperative course. With this burgeoning universe of molecular data, the anaesthesiologist-of-the-future will tailor their clinical management to an individual's multi-omic profile to optimise postoperative outcomes and long-term health.

Nanocarrier design–function relationship: The prodigious role of properties in regulating biocompatibility for drug delivery applications

The concept of drug delivery systems as a magic bullet for the delivery of bioactive compounds has emerged as a promising approach in the treatment of different diseases with significant advantages over the limitations of traditional methods. While nanocarrier-based drug delivery systems are the main advocates of drug uptake because they offer several advantages including reduced non-specific biodistribution, improved accumulation, and enhanced therapeutic efficiency; their safety and biocompatibility within cellular/tissue systems are therefore important for achieving the desired effect. The underlying power of "design-interplay chemistry" in modulating the properties and biocompatibility at the nanoscale level will direct the interaction with their immediate surrounding. Apart from improving the existing nanoparticle physicochemical properties, the balancing of the hosts' blood components interaction holds the prospect of conferring newer functions altogether. So far, this concept has been remarkable in achieving many fascinating feats in addressing many challenges in nanomedicine such as immune responses, inflammation, biospecific targeting and treatment, and so on. This review, therefore, provides a diverse account of the recent advances in the fabrication of biocompatible nano-drug delivery platforms for chemotherapeutic applications, as well as combination therapy, theragnostic, and other diseases that are of interest to scientists in the pharmaceutical industries. Thus, careful consideration of the "property of choice" would be an ideal way to realize specific functions from a set of delivery platforms. Looking ahead, there is an enormous prospect for nanoparticle properties in regulating biocompatibility.

Developing an Evidence-Based Nutrition Curriculum for Correctional Settings

People experiencing incarceration in the United States face numerous health disparities before, during, and after imprisonment, with prison conditions often exacerbating the severity of their health conditions. Within prisons, inadequate nutrition may contribute to the high prevalence of chronic disease such as diabetes and heart disease. This article discusses the development of an evidence-based nutrition curriculum for prison settings, informed by literature on current nutrition in prison, as well as previous health interventions designed to improve the health of incarcerated individuals. The curriculum was developed using guidelines for an effective health curriculum from the Centers for Disease Control and Prevention. Furthermore, this article discusses the theoretical foundations and effective pedagogies for teaching health materials in prison and provides further recommendations for improving nutrition in correctional institutions.

Studying metabolism with multi-organ chips: new tools for disease modelling, pharmacokinetics and pharmacodynamics

Non-clinical models to study metabolism including animal models and cell assays are often limited in terms of species translatability and predictability of human biology. This field urgently requires a push towards more physiologically accurate recapitulations of drug interactions and disease progression in the body. Organ-on-chip systems, specifically multi-organ chips (MOCs), are an emerging technology that is well suited to providing a species-specific platform to study the various types of metabolism (glucose, lipid, protein and drug) by recreating organ-level function. This review provides a resource for scientists aiming to study human metabolism by providing an overview of MOCs recapitulating aspects of metabolism, by addressing the technical aspects of MOC development and by providing guidelines for correlation with in silico models. The current state and challenges are presented for two application areas: (i) disease modelling and (ii) pharmacokinetics/pharmacodynamics. Additionally, the guidelines to integrate the MOC data into in silico models could strengthen the predictive power of the technology. Finally, the translational aspects of metabolizing MOCs are addressed, including adoption for personalized medicine and prospects for the clinic. Predictive MOCs could enable a significantly reduced dependence on animal models and open doors towards economical non-clinical testing and understanding of disease mechanisms.

From organ-on-chip to body-on-chip: The next generation of microfluidics platforms for in vitro drug efficacy and toxicity testing

The high failure rate in drug development is often attributed to the lack of accurate pre-clinical models that may lead to false discoveries and inconclusive data when the compounds are eventually tested in clinical phase. With the evolution of cell culture technologies, drug testing systems have widely improved, and today, with the emergence of microfluidics devices, drug screening seems to be at the dawn of an important revolution. An organ-on-chip allows the culture of living cells in continuously perfused microchambers to reproduce physiological functions of a particular tissue or organ. The advantages of such systems are not only their ability to recapitulate the complex biochemical interactions between different human cell types but also to incorporate physical forces, including shear stress and mechanical stretching or compression. To improve this model, and to reproduce the absorption, distribution, metabolism, and elimination process of an exogenous compound, organ-on-chips can even be linked fluidically to mimic physiological interactions between different organs, leading to the development of body-on-chips. Although these technologies are still at a young age and need to address a certain number of limitations, they already demonstrated their relevance to study the effect of drugs or toxins on organs, displaying a similar response to what is observed in vivo. The purpose of this review is to present the evolution from organ-on-chip to body-on-chip, examine their current use for drug testing and discuss their advantages and future challenges they will face in order to become an essential pillar of pharmaceutical research.

Immunity to SARS-CoV-2 induced by infection or vaccination

Adaptive immune responses play critical roles in viral clearance and protection against re-infection, and SARS-CoV-2 is no exception. What is exceptional is the rapid characterization of the immune response to the virus performed by researchers during the first 20 months of the pandemic. This has given us a more detailed understanding of SARS-CoV-2 compared to many viruses that have been with us for a long time. Furthermore, effective COVID-19 vaccines were developed in record time, and their rollout worldwide is already making a significant difference, although major challenges remain in terms of equal access. The pandemic has engaged scientists and the public alike, and terms such as seroprevalence, neutralizing antibodies, antibody escape and vaccine certificates have become familiar to a broad community. Here, we review key findings concerning B cell and antibody (Ab) responses to SARS-CoV-2, focusing on non-severe cases and anti-spike (S) Ab responses in particular, the latter being central to protective immunity induced by infection or vaccination. The emergence of viral variants that have acquired mutations in S acutely highlights the need for continued characterization of both emerging variants and Ab responses against these during the evolving pathogen-immune system arms race.

The antimicrobial and immunomodulatory effects of Ionophores for the treatment of human infection

Ionophores are a diverse class of synthetic and naturally occurring ion transporter compounds which demonstrate both direct and in-direct antimicrobial properties against a broad panel of bacterial, fungal, viral and parasitic pathogens. In addition, ionophores can regulate the host-immune response during communicable and non-communicable disease states. Although the clinical use of ionophores such as Amphotericin B, Bedaquiline and Ivermectin highlight the utility of ionophores in modern medicine, for many other ionophore compounds issues surrounding toxicity, bioavailability or lack of in vivo efficacy studies have hindered clinical development. The antimicrobial and immunomodulating properties of a range of compounds with characteristics of ionophores remain largely unexplored. As such, ionophores remain a latent therapeutic avenue to address both the global burden of antimicrobial resistance, and the unmet clinical need for new antimicrobial therapies. This review will provide an overview of the broad-spectrum antimicrobial and immunomodulatory properties of ionophores, and their potential uses in clinical medicine for combatting infection.

Pharmaceutical therapeutics for articular regeneration and restoration: state-of-the-art technology for screening small molecular drugs

Articular cartilage damage caused by sports injury or osteoarthritis (OA) has gained increased attention as a worldwide health burden. Pharmaceutical treatments are considered cost-effective means of promoting cartilage regeneration, but are limited by their inability to generate sufficient functional chondrocytes and modify disease progression. Small molecular chemical compounds are an abundant source of new pharmaceutical therapeutics for cartilage regeneration, as they have advantages in design, fabrication, and application, and, when used in combination, act as powerful tools for manipulating cellular fate. In this review, we present current achievements in the development of small molecular drugs for cartilage regeneration, particularly in the fields of chondrocyte generation and reversion of chondrocyte degenerative phenotypes. Several clinically or preclinically available small molecules, which have been shown to facilitate chondrogenesis, chondrocyte dedifferentiation, and cellular reprogramming, and subsequently ameliorate cartilage degeneration by targeting inflammation, matrix degradation, metabolism, and epigenetics, are summarized. Notably, this review introduces essential parameters for high-throughput screening strategies, including models of different chondrogenic cell sources, phenotype readout methodologies, and transferable advanced systems from other fields. Overall, this review provides new insights into future pharmaceutical therapies for cartilage regeneration.

Autologous Dendritic Cells in Combination With Chemotherapy Restore Responsiveness of T Cells in Breast Cancer Patients: A Single-Arm Phase I/II Trial

Introduction

Animal studies and preclinical studies in cancer patients suggest that the induction of immunogenic cell death (ICD) by neoadjuvant chemotherapy with doxorubicin and cyclophosphamide (NAC-AC) recovers the functional performance of the immune system. This could favor immunotherapy schemes such as the administration of antigen-free autologous dendritic cells (DCs) in combination with NAC-AC to profit as cryptic vaccine immunogenicity of treated tumors.

Objective

To explore the safety and immunogenicity of autologous antigen-free DCs administered to breast cancer patients (BCPs) in combination with NAC-AC.

Materials and Methods

A phase I/II cohort clinical trial was performed with 20 BCPs treated with NAC-AC [nine who received DCs and 11 who did not (control group)]. The occurrence of adverse effects and the functional performance of lymphocytes from BCPs before and after four cycles of NAC-AC receiving DCs or not were assessed using flow cytometry and compared with that from healthy donors (HDs). Flow cytometry analysis using manual and automated algorithms led us to examine functional performance and frequency of different lymphocyte compartments in response to a stimulus in vitro. This study was registered at clinicaltrials.gov (NCT03450044).

Results

No grade II or higher adverse effects were observed associated with the transfer of DCs to patients during NAC-AC. Interestingly, in response to the in vitro stimulation, deficient phosphorylation of Zap70 and AKT proteins observed before chemotherapy in most patients’ CD4 T cells significantly recovered after NAC-AC only in patients who received DCs.

Conclusions

The transfer of autologous DCs in combination with NAC-AC in BCPs is a safe procedure. That, in BCPs, the administration of DCs in combination with NAC-AC favors the recovery of the functional capacity of T cells suggests that this combination may potentiate the adjuvant effect of ICD induced by NAC-AC on T cells and, hence, potentiate the immunogenicity of tumors as cryptic vaccines.

Playing With Fire: Proinflammatory Virulence Mechanisms of Group A Streptococcus

Group A Streptococcus is an obligate human pathogen that is a major cause of infectious morbidity and mortality. It has a natural tropism for the oropharynx and skin, where it causes infections with excessive inflammation due to its expression of proinflammatory toxins and other virulence factors. Inflammation directly contributes to the severity of invasive infections, toxic shock syndrome, and the induction of severe post-infection autoimmune disease caused by autoreactive antibodies. This review discusses what is known about how the virulence factors of Group A Streptococcus induce inflammation and how this inflammation can promote disease. Understanding of streptococcal pathogenesis and the role of hyper-immune activation during infection may provide new therapeutic targets to treat the often-fatal outcome of severe disease.

Unraveling Gene Fusions for Drug Repositioning in High-Risk Neuroblastoma

High-risk neuroblastoma (NB) remains a significant therapeutic challenge facing current pediatric oncology patients. Structural variants such as gene fusions have shown an initial promise in enhancing mechanistic understanding of NB and improving survival rates. In this study, we performed a comprehensive in silico investigation on the translational ability of gene fusions for patient stratification and treatment development for high-risk NB patients. Specifically, three state-of-the-art gene fusion detection algorithms, including ChimeraScan, SOAPfuse, and TopHat-Fusion, were employed to identify the fusion transcripts in a RNA-seq data set of 498 neuroblastoma patients. Then, the 176 high-risk patients were further stratified into four different subgroups based on gene fusion profiles. Furthermore, Kaplan-Meier survival analysis was performed, and differentially expressed genes (DEGs) for the redefined high-risk group were extracted and functionally analyzed. Finally, repositioning candidates were enriched in each patient subgroup with drug transcriptomic profiles from the LINCS L1000 Connectivity Map. We found the number of identified gene fusions was increased from clinical the low-risk stage to the high-risk stage. Although the technical concordance of fusion detection algorithms was suboptimal, they have a similar biological relevance concerning perturbed pathways and regulated DEGs. The gene fusion profiles could be utilized to redefine high-risk patient subgroups with significant onset age of NB, which yielded the improved survival curves (Log-rank p value ≤ 0.05). Out of 48 enriched repositioning candidates, 45 (93.8%) have antitumor potency, and 24 (50%) were confirmed with either on-going clinical trials or literature reports. The gene fusion profiles have a discrimination power for redefining patient subgroups in high-risk NB and facilitate precision medicine-based drug repositioning implementation.

Opportunities and Challenges in Democratizing Immunology Datasets

The field of immunology is rapidly progressing toward a systems-level understanding of immunity to tackle complex infectious diseases, autoimmune conditions, cancer, and beyond. In the last couple of decades, advancements in data acquisition techniques have presented opportunities to explore untapped areas of immunological research. Broad initiatives are launched to disseminate the datasets siloed in the global, federated, or private repositories, facilitating interoperability across various research domains. Concurrently, the application of computational methods, such as network analysis, meta-analysis, and machine learning have propelled the field forward by providing insight into salient features that influence the immunological response, which was otherwise left unexplored. Here, we review the opportunities and challenges in democratizing datasets, repositories, and community-wide knowledge sharing tools. We present use cases for repurposing open-access immunology datasets with advanced machine learning applications and more.

Multi-Compartment Lymph-Node-on-a-Chip Enables Measurement of Immune Cell Motility in Response to Drugs

Organs On-a-Chip represent novel platforms for modelling human physiology and disease. The lymph node (LN) is a relevant immune organ in which B and T lymphocytes are spatially organized in a complex architecture, and it is the place where the immune response initiates. The present study addresses the utility of a recently designed LN-on-a-chip to dissect and understand the effect of drugs delivered to cells in a fluidic multicellular 3D setting that mimics the human LN. To do so, we analyzed the motility and viability of human B and T cells exposed to hydroxychloroquine (HCQ). We show that the innovative LN platform, which operates at a microscale level, allows real-time monitoring of co-cultured B and T cells by imaging, and supports cellular random movement. HCQ delivered to cells through a constant and continuous flow induces a reduction in T cell velocity while promotes persistent rotational motion. We also find that HCQ increases the production of reactive oxygen species in T cells. Taken together, these results highlight the potential of the LN-on-a-chip to be applied in drug screening and development, and in cellular dynamics studies.

Progress and applications of mass cytometry in sketching immune landscapes

Recently emerged mass cytometry (cytometry by time-of-flight [CyTOF]) technology permits the identification and quantification of inherently diverse cellular systems, and the simultaneous measurement of functional attributes at the single-cell resolution. By virtue of its multiplex ability with limited need for compensation, CyTOF has led a critical role in immunological research fields. Here, we present an overview of CyTOF, including the introduction of CyTOF principle and advantages that make it a standalone tool in deciphering immune mysteries. We then discuss the functional assays, introduce the bioinformatics to interpret the data yield via CyTOF, and depict the emerging clinical and research applications of CyTOF technology in sketching immune landscape in a wide variety of diseases.

Effects of Cell-Derived Microparticles on Immune Cells and Potential Implications in Clinical Medicine

In the past few years, interest has increased in cell-derived microparticles (MPs), which are defined by their size of from 0.1 to 1 μm, and can be derived from various cell types, including endothelial cells, leukocytes, red blood cells (RBCs), and platelets. These MPs carry negatively charged phosphatidylserine (PS) on their surfaces and proteins packaged from numerous cellular components. MPs that have been shed by the body can play important roles in the pathophysiology of diseases and can affect various biological systems. Among these systems, the immune components have been shown to be modulated by MPs. Therefore, understanding the roles of MPs in the immune system is crucial to developing alternative therapeutic treatments for diseases. This review describes the effects of MPs on various immune cells and provides plausible potential applications of the immune-modulating properties of MPs in clinical medicine.

Functional Gene Module-Based Identification of Phillyrin as an Anticardiac Fibrosis Agent

Cardiac fibrosis (CF) greatly influences the therapeutic effects of heart diseases and remains an urgent challenge in clinical therapy. Till now, only a few methods are used to find potential anti-CF drugs effectively. This study aimed to construct a gene functional module to represent the core pathological process of CF and screen antifibrotic agents capable of decreasing the expression of the gene functional module. First, three CF marker genes Postn, Ddr2, and Pdgfra were selected to identify the corresponding highest coexpressed genes in the genome-based transcriptional profiles of human hearts. Both the marker genes and the coexpressed genes formed the CF-related gene functional module. Second, the correlation of the module with the CF process was measured in a collection of gene expression profiles of heart diseases to evaluate the participation of the functional module in heart diseases. Third, the anti-CF effects of phillyrin were predicted by the enrichment analysis of the module in the phillyrin-induced transcriptional profile. Finally, the myocardial infarction animal model was used to validate the cardioprotective and anti-CF effects of phillyrin experimentally. The results showed that phillyrin was a novel antifibrotic agent in heart diseases.

Multi-Compartment 3D-Cultured Organ-on-a-Chip: Towards a Biomimetic Lymph Node for Drug Development

The interaction of immune cells with drugs and/or with other cell types should be mechanistically investigated in order to reduce attrition of new drug development. However, they are currently only limited technologies that address this need. In our work, we developed initial but significant building blocks that enable such immune-drug studies. We developed a novel microfluidic platform replicating the Lymph Node (LN) microenvironment called LN-on-a-chip, starting from design all the way to microfabrication, characterization and validation in terms of architectural features, fluidics, cytocompatibility, and usability. To prove the biomimetics of this microenvironment, we inserted different immune cell types in a microfluidic device, which showed an in-vivo-like spatial distribution. We demonstrated that the developed LN-on-a-chip incorporates key features of the native human LN, namely, (i) similarity in extracellular matrix composition, morphology, porosity, stiffness, and permeability, (ii) compartmentalization of immune cells within distinct structural domains, (iii) replication of the lymphatic fluid flow pattern, (iv) viability of encapsulated cells in collagen over the typical timeframe of immunotoxicity experiments, and (v) interaction among different cell types across chamber boundaries. Further studies with this platform may assess the immune cell function as a step forward to disclose the effects of pharmaceutics to downstream immunology in more physiologically relevant microenvironments.

Detection of Vasodilators From Herbal Components by a Transcriptome-Based Functional Gene Module Reference Approach

Vasodilatation is one of the key therapeutic strategies for the treatment of various cardiovascular diseases with high blood pressure. Therefore, development of drugs assisting blood vessel dilation is promising. It has been proven that many drugs display definite vasorelaxant effects. However, there are very few studies that systemically explore the effective vasodilators. In this work, we build a transcriptome-based functional gene module reference approach for systematic pursuit of agents with vasorelaxant effects. We firstly curate two functional gene modules that are specifically involved in positive and negative regulation of vascular diameter based on the known gene functional interaction knowledge. Secondly, a collection of gene expression profiles following herbal component treatment are collected from a public gene expression database. Then, the correlation of the gene modules is evaluated in each herbal component–induced gene expression profile by gene set enrichment analysis. The vasorelaxant effects of the candidate compounds can be predicted and ordered by the values of a defined index. Finally, the top 10 candidate compounds are experimentally tested for their vasorelaxant effects on vessel contraction induced by Phe in aortic rings. This strategy integrating different types of technologies is expected to help to create new opportunities for the development of novel vasodilators.

MetaMed: Linking Microbiota Functions with Medicine Therapeutics

Understanding how the human microbiome affects human health has consequences for treating disease and minimizing unwanted side effects in clinical research.

Understanding how the human microbiome affects human health has consequences for treating disease and minimizing unwanted side effects in clinical research. Here, we present MetaMed (http://metamed.rwebox.com/index), a novel and integrative system-wide correlation mapping system to link bacterial functions and medicine therapeutics, providing novel hypotheses for deep investigation of microbe therapeutic effects on human health. Furthermore, comprehensive relationships between microbes living in the environment and drugs were discovered, providing a rich source for discovering microbiota metabolites with great potential for pharmaceutical applications.

Critical period plasticity-related transcriptional aberrations in schizophrenia and bipolar disorder

Childhood critical periods of experience-dependent plasticity are essential for the development of environmentally appropriate behavior and cognition. Disruption of critical periods can alter development of normal function and confer risk for neurodevelopmental disorders. While genes and their expression relevant to neurodevelopment are associated with schizophrenia, the molecular relationship between schizophrenia and critical periods has not been assessed systematically. Here, we apply a transcriptome-based bioinformatics approach to assess whether genes associated with the human critical period for visual cortex plasticity, a well-studied model of cortical critical periods, are aberrantly expressed in schizophrenia and bipolar disorder. Across two dozen datasets encompassing 522 cases and 374 controls, we find that the majority show aberrations in expression of genes associated with the critical period. We observed both hyper- and hypo-critical period plasticity phenotypes at the transcriptome level, which partially mapped to drug candidates that reverse the disorder signatures in silico. Our findings indicate plasticity aberrations in schizophrenia and their treatment may need to be considered in the context of subpopulations with elevated and others reduced plasticity. Future work should leverage ongoing consortia RNA-sequencing efforts to tease out the sources of plasticity-related transcriptional aberrations seen in schizophrenia, including true biological heterogeneity, interaction between normal development/aging and the disorder, and medication history. Our study also urges innovation towards direct assessment of visual cortex plasticity in humans with schizophrenia to precisely deconstruct the role of plasticity in this disorder.

Synergy from gene expression and network mining (SynGeNet) method predicts synergistic drug combinations for diverse melanoma genomic subtypes

Systems biology perspectives are crucial for understanding the pathophysiology of complex diseases, and therefore hold great promise for the discovery of novel treatment strategies. Drug combinations have been shown to improve durability and reduce resistance to available first-line therapies in a variety of cancers; however, traditional drug discovery approaches are prohibitively cost and labor-intensive to evaluate large-scale matrices of potential drug combinations. Computational methods are needed to efficiently model complex interactions of drug target pathways and identify mechanisms underlying drug combination synergy. In this study, we employ a computational approach, SynGeNet (Synergy from Gene expression and Network mining), which integrates transcriptomics-based connectivity mapping and network centrality analysis to analyze disease networks and predict drug combinations. As an exemplar of a disease in which combination therapies demonstrate efficacy in genomic-specific contexts, we investigate malignant melanoma. We employed SynGeNet to generate drug combination predictions for each of the four major genomic subtypes of melanoma (BRAF, NRAS, NF1, and triple wild type) using publicly available gene expression and mutation data. We validated synergistic drug combinations predicted by our method across all genomic subtypes using results from a high-throughput drug screening study across. Finally, we prospectively validated the drug combination for BRAF-mutant melanoma that was top ranked by our approach, vemurafenib (BRAF inhibitor) + tretinoin (retinoic acid receptor agonist), using both in vitro and in vivo models of BRAF-mutant melanoma and RNA-sequencing analysis of drug-treated melanoma cells to validate the predicted mechanisms. Our approach is applicable to a wide range of disease domains, and, importantly, can model disease-relevant protein subnetworks in precision medicine contexts.

Computational Drug Repositioning for Gastric Cancer using Reversal Gene Expression Profiles

Treatment of gastric cancer (GC) often produces poor outcomes. Moreover, predicting which GC treatments will be effective remains challenging. Computational drug repositioning using public databases is a promising and efficient tool for discovering new uses for existing drugs. Here we used a computational reversal of gene expression approach based on effects on gene expression signatures by GC disease and drugs to explore new GC drug candidates. Gene expression profiles for individual GC tumoral and normal gastric tissue samples were downloaded from the Gene Expression Omnibus (GEO) and differentially expressed genes (DEGs) in GC were determined with a meta-signature analysis. Profiles drug activity and drug-induced gene expression were downloaded from the ChEMBL and the LINCS databases, respectively. Candidate drugs to treat GC were predicted using reversal gene expression score (RGES). Drug candidates including sorafenib, olaparib, elesclomol, tanespimycin, selumetinib, and ponatinib were predicted to be active for treatment of GC. Meanwhile, GC-related genes such as PLOD3, COL4A1, UBE2C, MIF, and PRPF5 were identified as having gene expression profiles that can be reversed by drugs. These findings support the use of a computational reversal gene expression approach to identify new drug candidates that can be used to treat GC.

Leveraging Big Data to Transform Drug Discovery

The surge of public disease and drug-related data availability has facilitated the application of computational methodologies to transform drug discovery. In the current chapter, we outline and detail the various resources and tools one can leverage in order to perform such analyses. We further describe in depth the in silico workflows of two recent studies that have identified possible novel indications of existing drugs. Lastly, we delve into the caveats and considerations of this process to enable other researchers to perform rigorous computational drug discovery experiments of their own.

Amelioration of Alzheimer's disease pathology and cognitive deficits by immunomodulatory agents in animal models of Alzheimer's disease

The most common age-related neurodegenerative disease is Alzheimer’s disease (AD) characterized by aggregated amyloid-β (Aβ) peptides in extracellular plaques and aggregated hyperphosphorylated tau protein in intraneuronal neurofibrillary tangles, together with loss of cholinergic neurons, synaptic alterations, and chronic inflammation within the brain. These lead to progressive impairment of cognitive function. There is evidence of innate immune activation in AD with microgliosis. Classically-activated microglia (M1 state) secrete inflammatory and neurotoxic mediators, and peripheral immune cells are recruited to inflammation sites in the brain. The few drugs approved by the US FDA for the treatment of AD improve symptoms but do not change the course of disease progression and may cause some undesirable effects. Translation of active and passive immunotherapy targeting Aβ in AD animal model trials had limited success in clinical trials. Treatment with immunomodulatory/anti-inflammatory agents early in the disease process, while not preventive, is able to inhibit the inflammatory consequences of both Aβ and tau aggregation. The studies described in this review have identified several agents with immunomodulatory properties that alleviated AD pathology and cognitive impairment in animal models of AD. The majority of the animal studies reviewed had used transgenic models of early-onset AD. More effort needs to be given to creat models of late-onset AD. The effects of a combinational therapy involving two or more of the tested pharmaceutical agents, or one of these agents given in conjunction with one of the cell-based therapies, in an aged animal model of AD would warrant investigation.

In Vitro Immune Organs-on-Chip for Drug Development: A Review

The current drug development practice lacks reliable and sensitive techniques to evaluate the immunotoxicity of drug candidates, i.e., their effect on the human immune system. This, in part, has resulted in a high attrition rate for novel drugs candidates. Organ-on-chip devices have emerged as key tools that permit the study of human physiology in controlled in vivo simulating environments. Furthermore, there has been a growing interest in developing the so called "body-on-chip" devices to better predict the systemic effects of drug candidates. This review describes existing biomimetic immune organs-on-chip, highlights their physiological relevance to drug development and discovery and emphasizes the need for developing comprehensive immune system-on-chip models. Such immune models can enhance the performance of novel drug candidates during clinical trials and contribute to reducing the high attrition rate as well as the high cost associated with drug development.

A Review of Drug Repositioning Based Chemical-induced Cell Line Expression Data

Drug repositioning is an important area of biomedical research. The drug repositioning studies have shifted to computational approaches. Large-scale perturbation databases, such as the Connectivity Map and the Library of Integrated Network-Based Cellular Signatures, contain a number of chemical-induced gene expression profiles and provide great opportunities for computational biology and drug repositioning. One reason is that the profiles provided by the Connectivity Map and the Library of Integrated Network-Based Cellular Signatures databases show an overall view of biological mechanism in drugs, diseases and genes. In this article, we provide a review of the two databases and their recent applications in drug repositioning.

Systems-Mapping of Herbal Effects on Complex Diseases Using the Network-Perturbation Signatures

The herbs have proven to hold great potential to improve people's health and wellness during clinical practice over the past millennia. However, herbal medicine for the personalized treatment of disease is still under investigation owing to the complex multi-component interactions in herbs. To reveal the valuable insights for herbal synergistic therapy, we have chosen Traditional Chinese Medicine (TCM) as a case to illustrate the art and science behind the complicated multi-molecular, multi-genes interaction systems, and how the good practices of herbal combination therapy are applicable to personalized treatment. Here, we design system-wide interaction map strategy to provide a generic solution to establish the links between diseases and herbs based on comprehensive testing of molecular signatures in herb-disease pairs. Firstly, we integrated gene expression profiles from 189 diseases to characterize the disease-pathological feature. Then, we generated the perturbation signatures from the huge chemical informatics data and pharmacological data for each herb, which were represented the targets affected by the ingredients in the herb. So that we could assess the effects of herbs on the individual. Finally, we integrated the data of 189 diseases and 502 herbs, yielding the optimal herbal combinations for the diseases based on the strategy, and verifying the reliability of the strategy through the permutation testing and literature verification. Furthermore, we propose a novel formula as a candidate therapeutic drugs of rheumatoid arthritis and demonstrate its therapeutic mechanism through the systematic analysis of the influencing targets and biological processes. Overall, this computational method provides a systematic approach, which blended herbal medicine and omics data sets, allowing for the development of novel drug combinations for complex human diseases.

A Large-Scale Gene Expression Intensity-Based Similarity Metric for Drug Repositioning

Biological systems often respond to a specific environmental or genetic perturbation without pervasive gene expression changes. Such robustness to perturbations, however, is not reflected on the current computational strategies that utilize gene expression similarity metrics for drug discovery and repositioning. Here we propose a new expression-intensity-based similarity metric that consistently achieved better performance than other state-of-the-art similarity metrics with respect to the gold-standard clustering of drugs with known mechanisms of action. The new metric directly emphasizes the genes exhibiting the greatest changes in expression in response to a perturbation. Using the new framework to systematically compare 3,332 chemical and 3,934 genetic perturbations across 10 cell types representing diverse cellular signatures, we identified thousands of recurrent and cell type-specific connections. We also experimentally validated two drugs identified by the analysis as potential topoisomerase inhibitors. The new framework is a valuable resource for hypothesis generation, functional testing, and drug repositioning.

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Intensity-based similarity metric surpasses other standard metrics in drug clustering

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This metric was applied to compare thousands of compounds for drug repurposing

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Two drugs are experimentally confirmed as potential topoisomerase inhibitors

Recent advances in small molecule based cancer immunotherapy

Immunotherapy has been increasingly utilized for the treatment of cancer. Currently available cancer immunotherapies mainly involve the use of antibodies, which have advantages in terms of pharmacodynamics such as efficacy and specificity, however, they exhibit disadvantages in regard to the pharmacokinetics including but not limited to poor tissue and tumor penetration, very long half-life, and the lack of oral bioavailability. Also they are immunogenic and may cause undesired side effects. In addition, they are difficult and expensive to produce. In contrast to therapeutic antibodies, small molecule immuno-oncology agents generally have favorable pharmacokinetics, for example, better oral bioavailability, higher tissue and tumor penetration, reasonable half-lives etc. Furthermore, some small molecules are highly selective and efficacious with benign toxicity profiles. Therefore, small molecule immuno-oncology agents have the potential to overcome the drawbacks of therapeutic antibodies, and they can complement existing therapeutic antibodies and may also be used in combination with antibodies to achieve synergistic effects. In this article, we summarize the current advances in the field of small molecule approaches in tumor immunology which include the small molecules in clinical trials and preclinical studies, and the reported crystal structures of small molecules and their target proteins as well as the binding interactions between small molecules and the targets. The tumorigenesis mechanism of different targets (the programmed cell death 1/programmed cell death ligand 1(PD1/PD-L1), retinoic acid-related orphan receptor-gamma t (RORγt), Chemokine receptor, Stimulator of Interferon Genes (Sting), Indoleamine 2,3-dioxygenase (IDO), toll-like receptors (TLR) etc.) are also elucidated.

Genome-Wide Expression Profiles Drive Discovery of Novel Compounds that Reduce Binge Drinking in Mice

Transcriptome-based drug discovery has identified new treatments for some complex diseases, but has not been applied to alcohol use disorder (AUD) or other psychiatric diseases, where there is a critical need for improved pharmacotherapies. High Drinking in the Dark (HDID-1) mice are a genetic model of AUD risk that have been selectively bred (from the HS/Npt line) to achieve intoxicating blood alcohol levels (BALs) after binge-like drinking. We compared brain gene expression of HDID-1 and HS/Npt mice, to determine a molecular signature for genetic risk for high intensity, binge-like drinking. Using multiple computational methods, we queried LINCS-L1000 (Library of Integrated Network-Based Cellular Signatures), a database containing gene expression signatures of thousands of compounds, to predict candidate drugs with the greatest potential to decrease alcohol consumption. Our analyses predicted novel compounds for testing, many with anti-inflammatory properties, providing further support for a neuroimmune mechanism of excessive alcohol drinking. We validated the top 2 candidates in vivo as a proof-of-concept. Terreic acid (a Bruton's tyrosine kinase inhibitor) and pergolide (a dopamine and serotonin receptor agonist) robustly reduced alcohol intake and BALs in HDID-1 mice, providing the first evidence for transcriptome-based drug discovery to target an addiction trait. Effective drug treatments for many psychiatric diseases are lacking, and the emerging tools and approaches outlined here offer researchers studying complex diseases renewed opportunities to discover new or repurpose existing compounds and expedite treatment options.

Clinical pharmacodynamic/exposure characterisation of the multikinase inhibitor ilorasertib (ABT-348) in a phase 1 dose-escalation trial

Background

Ilorasertib (ABT-348) inhibits Aurora and VEGF receptor (VEGFR) kinases. Patients with advanced solid tumours participated in a phase 1 dose-escalation trial to profile the safety, tolerability, and pharmacokinetics of ilorasertib.

Methods

Ilorasertib monotherapy was administered at 10–180 mg orally once daily (Arm I, n = 23), 40–340 mg orally twice daily (Arm II, n = 28), or 8–32 mg intravenously once daily (Arm III, n = 7), on days 1, 8, and 15 of each 28-day cycle.

Results

Dose-limiting toxicities were predominantly related to VEGFR inhibition. The most frequent treatment-emergent adverse events ( > 30%) were: fatigue (48%), anorexia (34%), and hypertension (34%). Pharmacodynamic markers suggested that ilorasertib engaged VEGFR2 and Aurora B kinase, with the VEGFR2 effects reached at lower doses and exposures than Aurora inhibition effects. In Arm II, one basal cell carcinoma patient (40 mg twice daily (BID)) and one patient with adenocarcinoma of unknown primary site (230 mg BID) had partial responses.

Conclusions

In patients with advanced solid tumours, ilorasertib treatment resulted in evidence of engagement of the intended targets and antitumour activity, but with maximum inhibition of VEGFR family kinases occurring at lower exposures than typically required for inhibition of Aurora B in tissue.

Clinical Trial Registration: NCT01110486

In vivo systems biology approaches to chronic immune/inflammatory pathophysiology

Systems biology offers an emphasis on integrative computational analysis of complex multi-component processes to enhance capability for predictive insights concerning operation of those processes. The immune system represents a prominent arena in which such processes are manifested for vital roles in physiology and pathology, encompassing dozens of cell types and hundreds of reciprocal interactions. Chronic, debilitating pathologies involving immune system dysregulation have become recognized as increasing in incidence over recent decades. While clinical consequences of immune dysregulation in such pathologies are well characterized, treatment options remain limited and focus on ameliorating symptoms. Because it is difficult to recapitulate more than a severely limited facet of the immune system in vitro, application of systems biology approaches to autoimmune and inflammatory pathophysiology in vivo has opened a new door toward discerning disease sub-groups and developing associated stratification strategies for patient treatment. In particular, early instances of these approaches have demonstrated advances in uncovering previously under-appreciated dysregulation of signaling networks between immune system and tissue cells, raising promise for improving upon current therapeutic approaches.

Small molecule immuno-oncology therapeutic agents

Treatment of cancer by activation of an antitumor immune response is now a widely practiced and well-accepted approach to therapy. However, despite dramatic responses in some patients, the high proportion of unresponsive patients points to a considerable unmet medical need. Although antibody therapies have led the way, small molecule immuno-oncology agents are close behind. This perspective provides an overview of some of the many small molecule approaches being explored. It encompasses small molecule modulators of validated targets such as programed cell death 1 (PD-1) as well as novel approaches still to be proven clinically.

Computational models for predicting drug responses in cancer research

The computational prediction of drug responses based on the analysis of multiple types of genome-wide molecular data is vital for accomplishing the promise of precision medicine in oncology. This will benefit cancer patients by matching their tumor characteristics to the most effective therapy available. As larger and more diverse layers of patient-related data become available, further demands for new bioinformatics approaches and expertise will arise. This article reviews key strategies, resources and techniques for the prediction of drug sensitivity in cell lines and patient-derived samples. It discusses major advances and challenges associated with the different model development steps. This review highlights major trends in this area, and will assist researchers in the assessment of recent progress and in the selection of approaches to emerging applications in oncology.

Individualized network-based drug repositioning infrastructure for precision oncology in the panomics era

Advances in next-generation sequencing technologies have generated the data supporting a large volume of somatic alterations in several national and international cancer genome projects, such as The Cancer Genome Atlas and the International Cancer Genome Consortium. These cancer genomics data have facilitated the revolution of a novel oncology drug discovery paradigm from candidate target or gene studies toward targeting clinically relevant driver mutations or molecular features for precision cancer therapy. This focuses on identifying the most appropriately targeted therapy to an individual patient harboring a particularly genetic profile or molecular feature. However, traditional experimental approaches that are used to develop new chemical entities for targeting the clinically relevant driver mutations are costly and high-risk. Drug repositioning, also known as drug repurposing, re-tasking or re-profiling, has been demonstrated as a promising strategy for drug discovery and development. Recently, computational techniques and methods have been proposed for oncology drug repositioning and identifying pharmacogenomics biomarkers, but overall progress remains to be seen. In this review, we focus on introducing new developments and advances of the individualized network-based drug repositioning approaches by targeting the clinically relevant driver events or molecular features derived from cancer panomics data for the development of precision oncology drug therapies (e.g. one-person trials) to fully realize the promise of precision medicine. We discuss several potential challenges (e.g. tumor heterogeneity and cancer subclones) for precision oncology. Finally, we highlight several new directions for the precision oncology drug discovery via biotherapies (e.g. gene therapy and immunotherapy) that target the 'undruggable' cancer genome in the functional genomics era.

Computational drug repositioning for rare diseases in the era of precision medicine

There are tremendous unmet needs in drug development for rare diseases. Computational drug repositioning is a promising approach and has been successfully applied to the development of treatments for diseases. However, how to utilize this knowledge and effectively conduct and implement computational drug repositioning approaches for rare disease therapies is still an open issue. Here, we focus on the means of utilizing accumulated genomic data for accelerating and facilitating drug repositioning for rare diseases. First, we summarize the current genome landscape of rare diseases. Second, we propose several promising bioinformatics approaches and pipelines for computational drug repositioning for rare diseases. Finally, we discuss recent regulatory incentives and other enablers in rare disease drug development and outline the remaining challenges.

Quantifying the relative immune cell activation from whole tissue/organ-derived differentially expressed gene data

Evaluation of immune responses in individual immune cell types is important for the development of new medicines. Here, we propose a computational method designated ICEPOP (Immune CEll POPulation) to estimate individual immune cell type responses from bulk tissue and organ samples. The relative gene responses are scored for each cell type by using the data from differentially expressed genes derived from control- vs drug-treated sample pairs, and the data from public databases including ImmGen and IRIS, which contain gene expression profiles of a variety of immune cells. By ICEPOP, we analysed cell responses induced by vaccine-adjuvants in the mouse spleen, and extended the analyses to human peripheral blood mononuclear cells and gut biopsy samples focusing on human papilloma virus vaccination and inflammatory bowel disease treatment with Infliximab. In both mouse and human datasets, our method reliably quantified the responding immune cell types and provided insightful information, demonstrating that our method is useful to evaluate immune responses from bulk sample-derived gene expression data. ICEPOP is available as an interactive web site (https://vdynamics.shinyapps.io/icepop/) and Python package (https://github.com/ewijaya/icepop).

Pancreas Cancer Precision Treatment Using Avatar Mice from a Bioinformatics Perspective

Pancreatic ductal adenocarcinoma (PDAC) is a leading cause of cancer-related death among solid malignancies. Unfortunately, PDAC lethality has not substantially decreased over the past 20 years. This aggressiveness is related to the genomic complexity and heterogeneity of PDAC, but also to the absence of an effective screening for the detection of early-stage tumors and a lack of efficient therapeutic options. Therefore, there is an urgent need to improve the arsenal of anti-PDAC drugs for an effective treatment of these patients. Patient-derived xenograft (PDX) mouse models represent a promising strategy to personalize PDAC treatment, offering a bench testing of candidate treatments and helping to select empirical treatments in PDAC patients with no therapeutic targets. Moreover, bioinformatics-based approaches have the potential to offer systematic insights into PDAC etiology predicting putatively actionable tumor-specific genomic alterations, identifying novel biomarkers and generating disease-associated gene expression signatures. This review focuses on recent efforts to individualize PDAC treatments using PDX models. Additionally, we discuss the current understanding of the PDAC genomic landscape and the putative druggable targets derived from mutational studies. PDAC molecular subclassifications and gene expression profiling studies are reviewed as well. Finally, latest bioinformatics methodologies based on somatic variant detection and prioritization, in silico drug response prediction, and drug repositioning to improve the treatment of advanced PDAC tumors are also covered.

Comparing structural and transcriptional drug networks reveals signatures of drug activity and toxicity in transcriptional responses

We performed an integrated analysis of drug chemical structures and drug-induced transcriptional responses. We demonstrated that a network representing three-dimensional structural similarities among 5452 compounds can be used to automatically group together drugs with similar scaffolds, physicochemical parameters and mode-of-action. We compared the structural network to a network representing transcriptional similarities among a subset of 1309 drugs for which transcriptional response were available in the Connectivity Map data set. Analysis of structurally similar, but transcriptionally different drugs sharing the same MOA enabled us to detect and remove weak and noisy transcriptional responses, greatly enhancing the reliability of transcription-based approaches to drug discovery and drug repositioning. Cardiac glycosides exhibited the strongest transcriptional responses with a significant induction of pathways related to epigenetic regulation, which suggests an epigenetic mechanism of action for these drugs. Drug classes with the weakest transcriptional responses tended to induce expression of cytochrome P450 enzymes, hinting at drug-induced drug resistance. Analysis of transcriptionally similar, but structurally different drugs with unrelated MOA, led us to the identification of a 'toxic' transcriptional signature indicative of lysosomal stress (lysosomotropism) and lipid accumulation (phospholipidosis) partially masking the target-specific transcriptional effects of these drugs. We found that this transcriptional signature is shared by 258 compounds and it is associated to the activation of the transcription factor TFEB, a master regulator of lysosomal biogenesis and autophagy. Finally, we built a predictive Random Forest model of these 258 compounds based on 128 physicochemical parameters, which should help in the early identification of potentially toxic drug candidates.

Transcriptional responses to drug treatment can reveal mechanism of action and off-target effects thus enabling drug repositioning, but only if measured in the appropriate cells at clinically relevant concentrations. A team led by Diego di Bernardo and Diego Medina generated a network representing structural similarities among compounds to automatically group together drugs with similar scaffolds and MOA. By comparing the structural drug network with a transcriptional drug network based on similarities in transcriptional response, the team observed broad differences between the two. This observation led to the identification of a transcriptional signature related lysosomal stress and phospholipidosis, and a physicochemical model to identify such compounds. These results provide general guidelines to prevent erroneous conclusion when using transcriptional responses of small molecules for drug discovery and drug repositioning