Disciplined approach to drug discovery and early development

Sustainable integration of artificial intelligence and machine learning approaches within the African infectious disease vaccine research and development ecosystem

Artificial Intelligence and Machine Learning (AI/ML) techniques, including reverse vaccinology and predictive models, have already been applied for developing vaccine candidates for COVID-19, HIV, and Hepatitis, streamlining the vaccine development lifecycle from discovery to deployment. The application of AI and ML technologies for improving heath interventions, including drug discovery and clinical development, are expanding across Africa, particularly in South Africa, Kenya, and Nigeria. Further initiatives are required however to expand AI/ML capabilities across the continent to ensure the development of a sustainable ecosystem including enhancing the requisite knowledge base, fostering collaboration between stakeholders, ensuring robust regulatory and ethical frameworks and investment in requisite infrastructure.

Genetic association of lipid-lowering drugs with aortic aneurysms: a Mendelian randomization study

AIMS

The lack of effective pharmacotherapies for aortic aneurysms (AA) is a persistent clinical challenge. Lipid metabolism plays an essential role in AA. However, the impact of lipid-lowering drugs on AA remains controversial. The study aimed to investigate the genetic association between lipid-lowering drugs and AA.

METHODS AND RESULTS

Our research used publicly available data on genome-wide association studies (GWASs) and expression quantitative trait loci (eQTL) studies. Genetic instruments, specifically eQTLs related to drug-target genes and SNPs (single nucleotide polymorphisms) located near or within the drug-target loci associated with low-density lipoprotein cholesterol (LDL-C), have been served as proxies for lipid-lowering medications. Drug-Target Mendelian Randomization (MR) study is used to determine the causal association between lipid-lowering drugs and different types of AA. The MR analysis revealed that higher expression of HMGCR (3-hydroxy-3-methylglutaryl coenzyme A reductase) was associated with increased risk of AA (OR = 1.58, 95% CI = 1.20-2.09, P = 1.20 × 10-03) and larger lumen size (aortic maximum area: OR = 1.28, 95% CI = 1.13-1.46, P = 1.48 × 10-04; aortic minimum area: OR = 1.26, 95% CI = 1.21-1.42, P = 1.78 × 10-04). PCSK9 (proprotein convertase subtilisin/kexin type 9) and CETP (cholesteryl ester transfer protein) show a suggestive relationship with AA (PCSK9: OR = 1.34, 95% CI = 1.10-1.63, P = 3.07 × 10-03; CETP: OR = 1.38, 95% CI = 1.06-1.80, P = 1.47 × 10-02). No evidence to support genetically mediated NPC1L1 (Niemann-Pick C1-Like 1) and LDLR (low-density lipoprotein cholesterol receptor) are associated with AA.

CONCLUSION

This study provides causal evidence for the genetic association between lipid-lowering drugs and AA. Higher gene expression of HMGCR, PCSK9, and CETP increases AA risk. Furthermore, HMGCR inhibitors may link with smaller aortic lumen size.

Sequential immunotherapy: towards cures for autoimmunity

Despite major progress in the treatment of autoimmune diseases in the past two decades, most therapies do not cure disease and can be associated with increased risk of infection through broad suppression of the immune system. However, advances in understanding the causes of autoimmune disease and clinical data from novel therapeutic modalities such as chimeric antigen receptor T cell therapies provide evidence that it may be possible to re-establish immune homeostasis and, potentially, prolong remission or even cure autoimmune diseases. Here, we propose a 'sequential immunotherapy' framework for immune system modulation to help achieve this ambitious goal. This framework encompasses three steps: controlling inflammation; resetting the immune system through elimination of pathogenic immune memory cells; and promoting and maintaining immune homeostasis via immune regulatory agents and tissue repair. We discuss existing drugs and those in development for each of the three steps. We also highlight the importance of causal human biology in identifying and prioritizing novel immunotherapeutic strategies as well as informing their application in specific patient subsets, enabling precision medicine approaches that have the potential to transform clinical care.

Exome sequence analysis identifies rare coding variants associated with a machine learning-based marker for coronary artery disease

Coronary artery disease (CAD) exists on a spectrum of disease represented by a combination of risk factors and pathogenic processes. An in silico score for CAD built using machine learning and clinical data in electronic health records captures disease progression, severity and underdiagnosis on this spectrum and could enhance genetic discovery efforts for CAD. Here we tested associations of rare and ultrarare coding variants with the in silico score for CAD in the UK Biobank, All of Us Research Program and BioMe Biobank. We identified associations in 17 genes; of these, 14 show at least moderate levels of prior genetic, biological and/or clinical support for CAD. We also observed an excess of ultrarare coding variants in 321 aggregated CAD genes, suggesting more ultrarare variant associations await discovery. These results expand our understanding of the genetic etiology of CAD and illustrate how digital markers can enhance genetic association investigations for complex diseases.

Reframing sepsis immunobiology for translation: towards informative subtyping and targeted immunomodulatory therapies

Sepsis is a common and deadly condition. Within the current model of sepsis immunobiology, the framing of dysregulated host immune responses into proinflammatory and immunosuppressive responses for the testing of novel treatments has not resulted in successful immunomodulatory therapies. Thus, the recent focus has been to parse observable heterogeneity into subtypes of sepsis to enable personalised immunomodulation. In this Personal View, we highlight that many fundamental immunological concepts such as resistance, disease tolerance, resilience, resolution, and repair are not incorporated into the current sepsis immunobiology model. The focus for addressing heterogeneity in sepsis should be broadened beyond subtyping to encompass the identification of deterministic molecular networks or dominant mechanisms. We explicitly reframe the dysregulated host immune responses in sepsis as altered homoeostasis with pathological disruption of immune-driven resistance, disease tolerance, resilience, and resolution mechanisms. Our proposal highlights opportunities to identify novel treatment targets and could enable successful immunomodulation in the future.

Causal inference in drug discovery and development

To discover new drugs is to seek and to prove causality. As an emerging approach leveraging human knowledge and creativity, data, and machine intelligence, causal inference holds the promise of reducing cognitive bias and improving decision-making in drug discovery. Although it has been applied across the value chain, the concepts and practice of causal inference remain obscure to many practitioners. This article offers a nontechnical introduction to causal inference, reviews its recent applications, and discusses opportunities and challenges of adopting the causal language in drug discovery and development.

Efficacy of metformin targets on cardiometabolic health in the general population and non-diabetic individuals: a Mendelian randomization study

BACKGROUND

Metformin shows beneficial effects on cardiometabolic health in diabetic individuals. However, the beneficial effects in the general population, especially in non-diabetic individuals are unclear. We aim to estimate the effects of perturbation of seven metformin targets on cardiometabolic health using Mendelian randomization (MR).

METHODS

Genetic variants close to metformin-targeted genes associated with expression of the corresponding genes and glycated haemoglobin (HbA1c) level were used to proxy therapeutic effects of seven metformin-related drug targets. Eight cardiometabolic phenotypes under metformin trials were selected as outcomes (average N = 466,947). MR estimates representing the weighted average effects of the seven effects of metformin targets on the eight outcomes were generated. One-sample MR was applied to estimate the averaged and target-specific effects in 338,425 non-diabetic individuals in UK Biobank.

FINDINGS

Genetically proxied averaged effects of five metformin targets, equivalent to a 0.62% reduction of HbA1c level, was associated with 37.8% lower risk of coronary artery disease (CAD) (odds ratio [OR] = 0.62, 95% confidence interval [CI] = 0.46-0.84), lower levels of body mass index (BMI) (β = -0.22, 95% CI = -0.35 to -0.09), systolic blood pressure (SBP) (β = -0.19, 95% CI = -0.28 to -0.09) and diastolic blood pressure (DBP) levels (β = -0.29, 95% CI = -0.39 to -0.19). One-sample MR suggested that the seven metformin targets showed averaged and target-specific beneficial effects on BMI, SBP and DBP in non-diabetic individuals.

INTERPRETATION

This study showed that perturbation of seven metformin targets has beneficial effects on BMI and blood pressure in non-diabetic individuals. Clinical trials are needed to investigate whether similar effects can be achieved with metformin medications.

FUNDING

Funding information is provided in the Acknowledgements.

A publication-wide association study (PWAS), historical language models to prioritise novel therapeutic drug targets

Most biomedical knowledge is published as text, making it challenging to analyse using traditional statistical methods. In contrast, machine-interpretable data primarily comes from structured property databases, which represent only a fraction of the knowledge present in the biomedical literature. Crucial insights and inferences can be drawn from these publications by the scientific community. We trained language models on literature from different time periods to evaluate their ranking of prospective gene-disease associations and protein-protein interactions. Using 28 distinct historical text corpora of abstracts published between 1995 and 2022, we trained independent Word2Vec models to prioritise associations that were likely to be reported in future years. This study demonstrates that biomedical knowledge can be encoded as word embeddings without the need for human labelling or supervision. Language models effectively capture drug discovery concepts such as clinical tractability, disease associations, and biochemical pathways. Additionally, these models can prioritise hypotheses years before their initial reporting. Our findings underscore the potential for extracting yet-to-be-discovered relationships through data-driven approaches, leading to generalised biomedical literature mining for potential therapeutic drug targets. The Publication-Wide Association Study (PWAS) enables the prioritisation of under-explored targets and provides a scalable system for accelerating early-stage target ranking, irrespective of the specific disease of interest.

Deep learning-based risk prediction for interventional clinical trials based on protocol design: A retrospective study

Success rate of clinical trials (CTs) is low, with the protocol design itself being considered a major risk factor. We aimed to investigate the use of deep learning methods to predict the risk of CTs based on their protocols. Considering protocol changes and their final status, a retrospective risk assignment method was proposed to label CTs according to low, medium, and high risk levels. Then, transformer and graph neural networks were designed and combined in an ensemble model to learn to infer the ternary risk categories. The ensemble model achieved robust performance (area under the receiving operator characteristic curve [AUROC] of 0.8453 [95% confidence interval: 0.8409-0.8495]), similar to the individual architectures but significantly outperforming a baseline based on bag-of-words features (0.7548 [0.7493-0.7603] AUROC). We demonstrate the potential of deep learning in predicting the risk of CTs from their protocols, paving the way for customized risk mitigation strategies during protocol design.

Predictive validity in drug discovery: what it is, why it matters and how to improve it

Successful drug discovery is like finding oases of safety and efficacy in chemical and biological deserts. Screens in disease models, and other decision tools used in drug research and development (R&D), point towards oases when they score therapeutic candidates in a way that correlates with clinical utility in humans. Otherwise, they probably lead in the wrong direction. This line of thought can be quantified by using decision theory, in which 'predictive validity' is the correlation coefficient between the output of a decision tool and clinical utility across therapeutic candidates. Analyses based on this approach reveal that the detectability of good candidates is extremely sensitive to predictive validity, because the deserts are big and oases small. Both history and decision theory suggest that predictive validity is under-managed in drug R&D, not least because it is so hard to measure before projects succeed or fail later in the process. This article explains the influence of predictive validity on R&D productivity and discusses methods to evaluate and improve it, with the aim of supporting the application of more effective decision tools and catalysing investment in their creation.

Integrated System Pharmacology Approaches to Elucidate Multi-Target Mechanism of Solanum surattense against Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most common malignant liver tumors with high mortality. Chronic hepatitis B and C viruses, aflatoxins, and alcohol are among the most common causes of hepatocellular carcinoma. The limited reported data and multiple spectra of pathophysiological mechanisms of HCC make it a challenging task and a serious economic burden in health care management. Solanum surattense (S. surattense) is the herbal plant used in many regions of Asia to treat many disorders including various types of cancer. Previous in vitro studies revealed the medicinal importance of S. surattense against hepatocellular carcinoma. However, the exact molecular mechanism of S. surattense against HCC still remains unclear. In vitro and in silico experiments were performed to find the molecular mechanism of S. surattense against HCC. In this study, the network pharmacology approach was used, through which multi-targeted mechanisms of S. surattense were explored against HCC. Active ingredients and potential targets of S. surattense found in HCC were figured out. Furthermore, the molecular docking technique was employed for the validation of the successful activity of bioactive constituents against potential genes of HCC. The present study investigated the active “constituent–target–pathway” networks and determined the tumor necrosis factor (TNF), epidermal growth factor receptor (EGFR), mammalian target of rapamycin (mTOR), Bcl-2-like protein 1(BCL2L1), estrogen receptor (ER), GTPase HRas, hypoxia-inducible factor 1-alpha (HIF1-α), Harvey Rat sarcoma virus, also known as transforming protein p21 (HRAS), and AKT Serine/Threonine Kinase 1 (AKT1), and found that the genes were influenced by active ingredients of S. surattense. In vitro analysis was also performed to check the anti-cancerous activity of S. surattense on human liver cells. The result showed that S. surattense appeared to act on HCC via modulating different molecular functions, many biological processes, and potential targets implicated in 11 different pathways. Furthermore, molecular docking was employed to validate the successful activity of the active compounds against potential targets. The results showed that quercetin was successfully docked to inhibit the potential targets of HCC. This study indicates that active constituents of S. surattense and their therapeutic targets are responsible for their pharmacological activities and possible molecular mechanisms for treating HCC. Lastly, it is concluded that active compounds of S. surattense act on potential genes along with their influencing pathways to give a network analysis in system pharmacology, which has a vital role in the development and utilization of drugs. The current study lays a framework for further experimental research and widens the clinical usage of S. surattense.

Advances in Organ-on-a-Chip Materials and Devices

The organ-on-a-chip (OoC) paves a way for biomedical applications ranging from preclinical to clinical translational precision. The current trends in the in vitro modeling is to reduce the complexity of human organ anatomy to the fundamental cellular microanatomy as an alternative of recreating the entire cell milieu that allows systematic analysis of medicinal absorption of compounds, metabolism, and mechanistic investigation. The OoC devices accurately represent human physiology in vitro; however, it is vital to choose the correct chip materials. The potential chip materials include inorganic, elastomeric, thermoplastic, natural, and hybrid materials. Despite the fact that polydimethylsiloxane is the most commonly utilized polymer for OoC and microphysiological systems, substitute materials have been continuously developed for its advanced applications. The evaluation of human physiological status can help to demonstrate using noninvasive OoC materials in real-time procedures. Therefore, this Review examines the materials used for fabricating OoC devices, the application-oriented pros and cons, possessions for device fabrication and biocompatibility, as well as their potential for downstream biochemical surface alteration and commercialization. The convergence of emerging approaches, such as advanced materials, artificial intelligence, machine learning, three-dimensional (3D) bioprinting, and genomics, have the potential to perform OoC technology at next generation. Thus, OoC technologies provide easy and precise methodologies in cost-effective clinical monitoring and treatment using standardized protocols, at even personalized levels. Because of the inherent utilization of the integrated materials, employing the OoC with biomedical approaches will be a promising methodology in the healthcare industry.

From drug repositioning to target repositioning: prediction of therapeutic targets using genetically perturbed transcriptomic signatures

Motivation

A critical element of drug development is the identification of therapeutic targets for diseases. However, the depletion of therapeutic targets is a serious problem.

Results

In this study, we propose the novel concept of target repositioning, an extension of the concept of drug repositioning, to predict new therapeutic targets for various diseases. Predictions were performed by a trans-disease analysis which integrated genetically perturbed transcriptomic signatures (knockdown of 4345 genes and overexpression of 3114 genes) and disease-specific gene transcriptomic signatures of 79 diseases. The trans-disease method, which takes into account similarities among diseases, enabled us to distinguish the inhibitory from activatory targets and to predict the therapeutic targetability of not only proteins with known target–disease associations but also orphan proteins without known associations. Our proposed method is expected to be useful for understanding the commonality of mechanisms among diseases and for therapeutic target identification in drug discovery.

Availability and implementation

Supplemental information and software are available at the following website [http://labo.bio.kyutech.ac.jp/~yamani/target_repositioning/].

Supplementary information

Supplementary data are available at Bioinformatics online.

FL118, acting as a 'molecular glue degrader', binds to dephosphorylates and degrades the oncoprotein DDX5 (p68) to control c-Myc, survivin and mutant Kras against colorectal and pancreatic cancer with high efficacy

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC), a difficult-to-treat cancer, is expected to become the second-largest cause of cancer-related deaths by 2030, while colorectal cancer (CRC) is the third most common cancer and the third leading cause of cancer deaths. Currently, there is no effective treatment for PDAC patients. The development of novel agents to effectively treat these cancers remains an unmet clinical need. FL118, a novel anticancer small molecule, exhibits high efficacy against cancers; however, the direct biochemical target of FL118 is unknown.

METHODS

FL118 affinity purification, mass spectrometry, Nanosep centrifugal device and isothermal titration calorimetry were used for identifying and confirming FL118 binding to DDX5/p68 and its binding affinity. Immunoprecipitation (IP), western blots, real-time reverse transcription PCR, gene silencing, overexpression (OE) and knockout (KO) were used for analysing gene/protein function and expression. Chromatin IP was used for analysing protein-DNA interactions. The 3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyltetrazolium bromid assay and human PDAC/CRC cell/tumour models were used for determining PDAC/CRC cell/tumour in vitro and in vivo growth.

RESULTS

We discovered that FL118 strongly binds to dephosphorylates and degrades the DDX5 oncoprotein via the proteasome degradation pathway without decreasing DDX5 mRNA. Silencing and OE of DDX5 indicated that DDX5 is a master regulator for controlling the expression of multiple oncogenic proteins, including survivin, Mcl-1, XIAP, cIAP2, c-Myc and mutant Kras. Genetic manipulation of DDX5 in PDAC cells affects tumour growth. PDAC cells with DDX5 KO are resistant to FL118 treatment. Our human tumour animal model studies further indicated that FL118 exhibits high efficacy to eliminate human PDAC and CRC tumours that have a high expression of DDX5, while FL118 exhibits less effectiveness in PDAC and CRC tumours with low DDX5 expression.

CONCLUSION

DDX5 is a bona fide FL118 direct target and can act as a biomarker for predicting PDAC and CRC tumour sensitivity to FL118. This would greatly impact FL118 precision medicine for patients with advanced PDAC or advanced CRC in the clinic. FL118 may act as a 'molecular glue degrader' to directly glue DDX5 and ubiquitination regulators together to degrade DDX5.

Supporting Innovation in Early-Stage Pharmaceutical Development Decisions

Pharmaceutical companies have frequent portfolio reviews to monitor development progress and prioritize development assets. The earliest assets are drug candidates whose efficacy is unknown and whose effects on the human body have yet to be fully investigated. These assets are characterized by a high degree of uncertainty in reaching the market and in being used in clinical practice. In addition, not all potential applications are foreseen and can often be very different. In the absence of satisfactory methods for making decisions on resource allocation among early development assets, decision makers focus almost exclusively on assessments of an asset’s probability of technical success. This study proposes a more holistic methodology to support early-stage pharmaceutical development decisions using value-focused thinking and multicriteria decision making. The methodology operates within the decision quality framework and provides a consistent evaluation of various early development assets across a diverse set of disease areas. This combination of concepts and methodologies has been implemented and proven valuable at Bayer Pharmaceuticals, which needed a new, more robust decision-making process for early development. Thus, this study discusses how to enable concrete trade-offs at the level of corporate objectives to align, communicate, and translate corporate strategy into portfolio strategy. In addition, this study presents learnings for decision analysts and decision makers in the pharmaceutical industry on how to develop a set of fundamental objectives, how to create scales to operationalize these objectives, and how to take steps to debias an organizational decision-making process.

Novel Molecular Networks and Regulatory MicroRNAs in Type 2 Diabetes Mellitus: Multiomics Integration and Interactomics Study

BACKGROUND

Type 2 diabetes mellitus (T2DM) is a metabolic disorder with severe comorbidities. A multiomics approach can facilitate the identification of novel therapeutic targets and biomarkers with proper validation of potential microRNA (miRNA) interactions.

OBJECTIVE

The aim of this study was to identify significant differentially expressed common target genes in various tissues and their regulating miRNAs from publicly available Gene Expression Omnibus (GEO) data sets of patients with T2DM using in silico analysis.

METHODS

Using differentially expressed genes (DEGs) identified from 5 publicly available T2DM data sets, we performed functional enrichment, coexpression, and network analyses to identify pathways, protein-protein interactions, and miRNA-mRNA interactions involved in T2DM.

RESULTS

We extracted 2852, 8631, 5501, 3662, and 3753 DEGs from the expression profiles of GEO data sets GSE38642, GSE25724, GSE20966, GSE26887, and GSE23343, respectively. DEG analysis showed that 16 common genes were enriched in insulin secretion, endocrine resistance, and other T2DM-related pathways. Four DEGs, MAML3, EEF1D, NRG1, and CDK5RAP2, were important in the cluster network regulated by commonly targeted miRNAs (hsa-let-7b-5p, hsa-mir-155-5p, hsa-mir-124-3p, hsa-mir-1-3p), which are involved in the advanced glycation end products (AGE)-receptor for advanced glycation end products (RAGE) signaling pathway, culminating in diabetic complications and endocrine resistance.

CONCLUSIONS

This study identified tissue-specific DEGs in T2DM, especially pertaining to the heart, liver, and pancreas. We identified a total of 16 common DEGs and the top four common targeting miRNAs (hsa-let-7b-5p, hsa-miR-124-3p, hsa-miR-1-3p, and has-miR-155-5p). The miRNAs identified are involved in regulating various pathways, including the phosphatidylinositol-3-kinase-protein kinase B, endocrine resistance, and AGE-RAGE signaling pathways.

Discovery and Optimization of Pyrazole Amides as Inhibitors of ELOVL1

Accumulation of very long chain fatty acids (VLCFAs) due to defects in ATP binding cassette protein D1 (ABCD1) is thought to underlie the pathologies observed in adrenoleukodystrophy (ALD). Pursuing a substrate reduction approach based on the inhibition of elongation of very long chain fatty acid 1 enzyme (ELOVL1), we explored a series of thiazole amides that evolved into compound 27─a highly potent, central nervous system (CNS)-penetrant compound with favorable in vivo pharmacokinetics. Compound 27 selectively inhibits ELOVL1, reducing C26:0 VLCFA synthesis in ALD patient fibroblasts, lymphocytes, and microglia. In mouse models of ALD, compound 27 treatment reduced C26:0 VLCFA concentrations to near-wild-type levels in blood and up to 65% in the brain, a disease-relevant tissue. Preclinical safety findings in the skin, eye, and CNS precluded progression; the origin and relevance of these findings require further study. ELOVL1 inhibition is an effective approach for normalizing VLCFAs in models of ALD.

Integration of Real-World Data and Genetics to Support Target Identification and Validation

Even modest improvements in the probability of success of selecting drug targets which are ultimately approved can substantially reduce the costs of research and development. Drug targets with human genetic evidence of disease association are twice as likely to lead to approved drugs. A key enabler of identifying and validating these genetically validated targets is access to association results from genome-wide genotyping, whole-exome sequencing, and whole-genome sequencing studies with observable traits (often diseases) across large numbers of individuals. Today, linkage between genotype and real-world data (RWD) provides significant opportunities to not only increase the statistical power of genome-wide association studies by ascertaining additional cases for diseases of interest, but also to improve diversity and coverage of association studies across the disease phenome. As RWD-genetics linked resources continue to grow in diversity of participants, breadth of data captured, length of observation, and number of participants, there is a greater need to leverage the experience of RWD experts, clinicians, and highly experienced geneticists together to understand which lessons and frameworks from general research using RWD sources are relevant to improve genetics-driven drug discovery and development. This paper describes new challenges and opportunities for phenotypes enabled by diverse RWD sources, considerations in the use of RWD phenotypes for disease gene identification across the disease phenome, and challenges and opportunities in leveraging RWD phenotypes in target validation. The paper concludes with views on the future directions for phenotype development using RWD, and key questions requiring further research and development to advance this nascent field.

Enhancing preclinical drug discovery with artificial intelligence

Artificial intelligence (AI) is becoming an integral part of drug discovery. It has the potential to deliver across the drug discovery and development value chain, starting from target identification and reaching through clinical development. In this review, we provide an overview of current AI technologies and a glimpse of how AI is reimagining preclinical drug discovery by highlighting examples where AI has made a real impact. Considering the excitement and hyperbole surrounding AI in drug discovery, we aim to present a realistic view by discussing both opportunities and challenges in adopting AI in drug discovery.

TrendyGenes, a computational pipeline for the detection of literature trends in academia and drug discovery

Target identification and prioritisation are prominent first steps in modern drug discovery. Traditionally, individual scientists have used their expertise to manually interpret scientific literature and prioritise opportunities. However, increasing publication rates and the wider routine coverage of human genes by omic-scale research make it difficult to maintain meaningful overviews from which to identify promising new trends. Here we propose an automated yet flexible pipeline that identifies trends in the scientific corpus which align with the specific interests of a researcher and facilitate an initial prioritisation of opportunities. Using a procedure based on co-citation networks and machine learning, genes and diseases are first parsed from PubMed articles using a novel named entity recognition system together with publication date and supporting information. Then recurrent neural networks are trained to predict the publication dynamics of all human genes. For a user-defined therapeutic focus, genes generating more publications or citations are identified as high-interest targets. We also used topic detection routines to help understand why a gene is trendy and implement a system to propose the most prominent review articles for a potential target. This TrendyGenes pipeline detects emerging targets and pathways and provides a new way to explore the literature for individual researchers, pharmaceutical companies and funding agencies.

Plasmonic Interferometers as TREM2 Sensors for Alzheimer's Disease

We report an effective surface immobilization protocol for capture of Triggering Receptor Expressed on Myeloid Cells 2 (TREM2), a receptor whose elevated concentration in cerebrospinal fluid has recently been associated with Alzheimer's disease (AD). We employ the proposed surface functionalization scheme to design, fabricate, and assess a biochemical sensing platform based on plasmonic interferometry that is able to detect physiological concentrations of TREM2 in solution. These findings open up opportunities for label-free biosensing of TREM2 in its soluble form in various bodily fluids as an early indicator of the onset of clinical dementia in AD. We also show that plasmonic interferometry can be a powerful tool to monitor and optimize surface immobilization schemes, which could be applied to develop other relevant antibody tests.

Modelling human liver microphysiology on a chip through a finite element based design approach

Organ-on-a-chip (OoaC) are microfluidic devices capable of growing living tissue and replicate the intricate microenvironments of human organs in vitro, being heralded as having the potential to revolutionize biological research and healthcare by providing unprecedented control over fluid flow, relevant tissue to volume ratio, compatibility with high-resolution content screening and a reduced footprint. Finite element modelling is proven to be an efficient approach to simulate the microenvironments of OoaC devices, and may be used to study the existing correlations between geometry and hydrodynamics, towards developing devices of greater accuracy. The present work aims to refine a steady-state gradient generator for the development of a more relevant human liver model. For this purpose, the finite element method was used to simulate the device and predict which design settings, expressed by individual parameters, would better replicate in vitro the oxygen gradients found in vivo within the human liver acinus. To verify the model's predictive capabilities, two distinct examples were replicated from literature. Finite element analysis enabled obtaining an ideal solution, designated as liver gradient-on-a-chip, characterised by a novel way to control gradient generation, from which it was possible to determine concentration values ranging between 3% and 12%, thus providing a precise correlation with in vivo oxygen zonation, comprised between 3%-5% and 10%-12% within respectively the perivenous and periportal zones of the human liver acinus. Shear stress was also determined to average the value of 0.037 Pa, and therefore meet the interval determined from literature to enhance liver tissue culture, comprised between 0.01 - 0.05 Pa.

Translational Scoring of Candidate Treatments for Alzheimer's Disease: A Systematic Approach

BACKGROUND

There are many failures in treatment development for Alzheimer's disease (AD). Some of these failures are the result of development programs that lacked critical information about candidate drugs as these were advanced from one phase of development to the next. Translational scoring (TS) has been proposed as a means of increasing the rigor with which treatment development programs are executed. Previously, these approaches were not specific to AD or to the phase of drug development. Detailed information on the characteristics needed to advance a candidate agent from one phase to the next is the basis for success in subsequent phases.

SUMMARY

The TS approach is presented with a score range of 0-25 for agents entering phases 1, 2, and 3 of development and those that have completed phase 3 and are being considered for regulatory review. Each phase has 5 essential categories scored from 0-5 indicating the completeness of the data available when the agent is being considered for promotion to the next phase. Lower scores suggest that the development program should be reexamined for missing information while higher scores increase the confidence that the agent has the potential to succeed in the next phase. Scoring guidelines are provided and examples of scores for drugs in recent development programs are provided to illustrate the principles of TS. Key Messages: Successful development of drugs for AD treatment requires disciplined informed decision-making at each phase of development. TS is a methodology for more rigorous drug development to help ensure that inadequately characterized drugs are not advanced and that the development platform at each phase is optimal to support success at the next phase.

Facing the urgency of therapies for progressive MS - a Progressive MS Alliance proposal

Therapies for infiltrative inflammation in multiple sclerosis (MS) have advanced greatly, but neurodegeneration and compartmentalized inflammation remain virtually untargeted as in other diseases of the nervous system. Consequently, many therapies are available for the relapsing-remitting form of MS, but the progressive forms remain essentially untreated. The objective of the International Progressive MS Alliance is to expedite the development of effective therapies for progressive MS through new initiatives that foster innovative thinking and concrete advancements. Based on these principles, the Alliance is developing a new funding programme that will focus on experimental medicine trials. Here, we discuss the reasons behind the focus on experimental medicine trials, the strengths and weaknesses of these approaches and of the programme, and why we hope to advance therapies while improving the understanding of progression in MS. We are soliciting public and academic feedback, which will help shape the programme and future strategies of the Alliance.

Mining Natural Compounds to Target WNT Signaling: Land and Sea Tales

WNT signaling plays paramount roles in organism development, physiology, and disease, representing a highly attractive target for drug development. However, no WNT-modulating drugs have been approved, with several candidates trudging through the early clinical trials. This delay instigates alternative approaches to discover WNT-modulating drugs. Natural products were the source of therapeutics for centuries, but the chemical diversity they offer, especially when looking at different taxonomic groups and habitats, is still to a large extent unexplored. These considerations urge researchers to screen natural compounds for the WNT-modulatory activities. Since several reviews on such endeavors exist, we here have attempted to present these efforts as "Land and sea tales" (citing the book title by Rudyard Kipling) superimposing them onto the traditional pipeline of drug discovery and early development. In doing so, we illustrate each step of the pipeline with case studies stemming from our own research. It will become obvious that several steps of the pipeline need to be modified when applied to natural products rather than to synthetic libraries. Yet the main message of this chapter is that natural compounds represent a powerful source for the WNT signaling modulators and can be developed towards drug candidates against WNT-dependent maladies.

A case study of foliglurax, the first clinical mGluR4 PAM for symptomatic treatment of Parkinson's disease: translational gaps or a failing industry innovation model?

INTRODUCTION

Approximately 40% of Parkinson's disease (PD) patients that take mostly dopamine receptor agonists for motor fluctuations, experience the return of symptoms between regular doses. This is a phenomenon known as 'OFF periods.' Positive allosteric modulators (PAMs) of metabotropic glutamate receptor 4 (mGluR4) are a promising non-dopaminergic mechanism with potential to address the unmet need of patients suffering from OFF periods. Foliglurax is the first mGluR4 PAM that has advanced into clinical testing in PD patients.

AREAS COVERED

We summarize the chemistry, pharmacokinetics, and preclinical pharmacology of foliglurax. Translational PET imaging studies, clinical efficacy data, and a competitive landscape analysis of available therapies are presented to the readers. In this Perspective article, foliglurax is used as a case study to illustrate the inherent R&D challenges that companies face when developing drugs. These challenges include the delivery of drugs acting through novel mechanisms, long-term scientific investment, and commercial success and shorter-term positive financial returns.

EXPERT OPINION

Failure to meet the primary and secondary endpoints in a Phase 2 study led Lundbeck to discontinue the development of foliglurax. Understanding the evidence supporting compound progression into Phase 2 will enable the proper assessment of the therapeutic potential of mGluR4 PAMs.

Human pluripotent stem cell-derived models and drug screening in CNS precision medicine

Development of effective therapeutics for neurological disorders has historically been challenging partly because of lack of accurate model systems in which to investigate disease etiology and test new therapeutics at the preclinical stage. Human stem cells, particularly patient-derived induced pluripotent stem cells (iPSCs) upon differentiation, have the ability to recapitulate aspects of disease pathophysiology and are increasingly recognized as robust scalable systems for drug discovery. We review advances in deriving cellular models of human central nervous system (CNS) disorders using iPSCs along with strategies for investigating disease-relevant phenotypes, translatable biomarkers, and therapeutic targets. Given their potential to identify novel therapeutic targets and leads, we focus on phenotype-based, small-molecule screens employing human stem cell-derived models. Integrated efforts to assemble patient iPSC-derived cell models with deeply annotated clinicopathological data, along with molecular and drug-response signatures, may aid in the stratification of patients, diagnostics, and clinical trial success, shifting translational science and precision medicine approaches. A number of remaining challenges, including the optimization of cost-effective, large-scale culture of iPSC-derived cell types, incorporation of aging into neuronal models, as well as robustness and automation of phenotypic assays to support quantitative drug efficacy, toxicity, and metabolism testing workflows, are covered. Continued advancement of the field is expected to help fully humanize the process of CNS drug discovery.

Water-Templated, Polysaccharide-rich Bioartificial 3D Microarchitectures as Extra-Cellular Matrix Bioautomatons

This is the first report of exploiting the "quasi-spherical" shape of water molecules for recapitulating a true human extracellular matrix (ECM). Herein, water behaved as a quasi-spherical porogen, for engineering polysaccharide-rich and chemically defined 3D-microarchitecture, with semi-interpenetrating networks (S-IPNs). Furthermore, their viscoelastic behavior along with a heterogeneous, fibroporous morphology, facilitated instructive, self-remodeling of the bioartificial scaffolds, thence effectively permitting and promoting the growth of 3D tumor spheroids of divergent origins. The hybrid composites displayed reproducible, uniform tumor spheroids with a Z-depth of ∼65 ± 2 μm in case of human adenocarcinoma (DLD-1) and ∼54 ± 3 μm for human glioblastoma cells (U-251) (vs. nonuniform spheroids, on Agarose matrix). Thereafter, their capacity for anticancer drug screening was examined using limited cancer drugs. The conflicting drug screening results for Etoposide's reduced efficacy on glioblastoma cells cultured on our 3D matrix could be ascribed to decreased drug access and thus lower ingression. Nonetheless, adenocarcinoma's resistance to Camptothecin was paralleled. Moreover, their potential for real-time, high-content, phenotypic precision oncology was affirmed by the exceptional transparency of the synthesized composite. Since this 3D microarchitecture typifies ECM bioautomaton, this matrix can also be wielded for precision oncology.

Being Precise About Precision Medicine: What Should Value Frameworks Incorporate to Address Precision Medicine? A Report of the Personalized Precision Medicine Special Interest Group

Precision medicine is a dynamic area embracing a diverse and increasing type of approaches that allow the targeting of new medicines, screening programs or preventive healthcare strategies, which include the use of biologic markers or complex tests driven by algorithms also potentially taking account of patient preferences. The International Society for Pharmacoeconomics and Outcome Research expanded its current work around precision medicine to (1) describe the evolving paradigm of precision medicine with examples of current and evolving applications, (2) describe key stakeholders perspectives on the value of precision medicine in their respective domains, and (3) define the core factors that should be considered in a value assessment framework for precision medicine. With the ultimate goal of improving health of well-defined patient groups, precision medicine will affect all stakeholders in the healthcare system at multiple levels spanning the individual perspective to the societal perspective. For an efficient, timely and practical precision medicine value assessment framework, it will be important to address these multiple perspectives through building consensus among the stakeholders for robust procedures and measures of value aspects, including performance of precision mechanism; aligned reimbursement processes of precision mechanism and subsequent treatment; transparent expectations for evidence requirements and study designs adequately matched to the intended use of the precision mechanism and to the smaller target patient populations; recognizing the potential range of value-generation such as ruling-in and ruling-out decisions.

Translational strategies in drug development for knee osteoarthritis

Osteoarthritis (OA) is a common disease worldwide with large unmet medical needs. To bring innovative treatments to OA patients, we at Merck have implemented a comprehensive strategy for drug candidate evaluation. We have a clear framework for decision-making in our preclinical pipeline, to design our clinical proof-of-concept trials for OA patients. We have qualified our strategy to define and refine dose and dosing regimen, for treatments administered either systemically or intra-articularly (IA). We do this through preclinical in vitro and in vivo studies, and by back-translating results from clinical studies in OA patients.

Applications of Functional Genomics for Drug Discovery

Many diseases, such as diabetes, autoimmune diseases, cancer, and neurological disorders, are caused by a dysregulation of a complex interplay of genes. Genome-wide association studies have identified thousands of disease-linked polymorphisms in the human population. However, detailing the causative gene expression or functional changes underlying those associations has been elusive in many cases. Functional genomics is an emerging field of research that aims to deconvolute the link between genotype and phenotype by making use of large -omic data sets and next-generation gene and epigenome editing tools to perturb genes of interest. Here we review how functional genomic tools can be used to better understand the biological interplay between genes, improve disease modeling, and identify novel drug targets. Incorporation of functional genomic capabilities into conventional drug development pipelines is predicted to expedite the development of first-in-class therapeutics.

Therapies for rare diseases: therapeutic modalities, progress and challenges ahead

Most rare diseases still lack approved treatments despite major advances in research providing the tools to understand their molecular basis, as well as legislation providing regulatory and economic incentives to catalyse the development of specific therapies. Addressing this translational gap is a multifaceted challenge, for which a key aspect is the selection of the optimal therapeutic modality for translating advances in rare disease knowledge into potential medicines, known as orphan drugs. With this in mind, we discuss here the technological basis and rare disease applicability of the main therapeutic modalities, including small molecules, monoclonal antibodies, protein replacement therapies, oligonucleotides and gene and cell therapies, as well as drug repurposing. For each modality, we consider its strengths and limitations as a platform for rare disease therapy development and describe clinical progress so far in developing drugs based on it. We also discuss selected overarching topics in the development of therapies for rare diseases, such as approval statistics, engagement of patients in the process, regulatory pathways and digital tools.

Improving the odds of drug development success through human genomics: modelling study

Lack of efficacy in the intended disease indication is the major cause of clinical phase drug development failure. Explanations could include the poor external validity of pre-clinical (cell, tissue, and animal) models of human disease and the high false discovery rate (FDR) in preclinical science. FDR is related to the proportion of true relationships available for discovery (γ), and the type 1 (false-positive) and type 2 (false negative) error rates of the experiments designed to uncover them. We estimated the FDR in preclinical science, its effect on drug development success rates, and improvements expected from use of human genomics rather than preclinical studies as the primary source of evidence for drug target identification. Calculations were based on a sample space defined by all human diseases - the 'disease-ome' - represented as columns; and all protein coding genes - 'the protein-coding genome'- represented as rows, producing a matrix of unique gene- (or protein-) disease pairings. We parameterised the space based on 10,000 diseases, 20,000 protein-coding genes, 100 causal genes per disease and 4000 genes encoding druggable targets, examining the effect of varying the parameters and a range of underlying assumptions, on the inferences drawn. We estimated γ, defined mathematical relationships between preclinical FDR and drug development success rates, and estimated improvements in success rates based on human genomics (rather than orthodox preclinical studies). Around one in every 200 protein-disease pairings was estimated to be causal (γ = 0.005) giving an FDR in preclinical research of 92.6%, which likely makes a major contribution to the reported drug development failure rate of 96%. Observed success rate was only slightly greater than expected for a random pick from the sample space. Values for γ back-calculated from reported preclinical and clinical drug development success rates were also close to the a priori estimates. Substituting genome wide (or druggable genome wide) association studies for preclinical studies as the major information source for drug target identification was estimated to reverse the probability of late stage failure because of the more stringent type 1 error rate employed and the ability to interrogate every potential druggable target in the same experiment. Genetic studies conducted at much larger scale, with greater resolution of disease end-points, e.g. by connecting genomics and electronic health record data within healthcare systems has the potential to produce radical improvement in drug development success rate.

Bioelectric Control of Metastasis in Solid Tumors

As the leading cause of death in cancer, there is an urgent need to develop treatments to target the dissemination of primary tumor cells to secondary organs, known as metastasis. Bioelectric signaling has emerged in the last century as an important controller of cell growth, and with the development of current molecular tools we are now beginning to identify its role in driving cell migration and metastasis in a variety of cancer types. This review summarizes the currently available research for bioelectric signaling in solid tumor metastasis. We review the steps of metastasis and discuss how these can be controlled by bioelectric cues at the level of a cell, a population of cells, and the tissue. The role of ion channel, pump, and exchanger activity and ion flux is discussed, along with the importance of the membrane potential and the relationship between ion flux and membrane potential. We also provide an overview of the evidence for control of metastasis by external electric fields (EFs) and draw from examples in embryogenesis and regeneration to discuss the implications for endogenous EFs. By increasing our understanding of the dynamic properties of bioelectric signaling, we can develop new strategies that target metastasis to be translated into the clinic.

The "rights" of precision drug development for Alzheimer's disease

There is a high rate of failure in Alzheimer's disease (AD) drug development with 99% of trials showing no drug-placebo difference. This low rate of success delays new treatments for patients and discourages investment in AD drug development. Studies across drug development programs in multiple disorders have identified important strategies for decreasing the risk and increasing the likelihood of success in drug development programs. These experiences provide guidance for the optimization of AD drug development. The "rights" of AD drug development include the right target, right drug, right biomarker, right participant, and right trial. The right target identifies the appropriate biologic process for an AD therapeutic intervention. The right drug must have well-understood pharmacokinetic and pharmacodynamic features, ability to penetrate the blood-brain barrier, efficacy demonstrated in animals, maximum tolerated dose established in phase I, and acceptable toxicity. The right biomarkers include participant selection biomarkers, target engagement biomarkers, biomarkers supportive of disease modification, and biomarkers for side effect monitoring. The right participant hinges on the identification of the phase of AD (preclinical, prodromal, dementia). Severity of disease and drug mechanism both have a role in defining the right participant. The right trial is a well-conducted trial with appropriate clinical and biomarker outcomes collected over an appropriate period of time, powered to detect a clinically meaningful drug-placebo difference, and anticipating variability introduced by globalization. We lack understanding of some critical aspects of disease biology and drug action that may affect the success of development programs even when the "rights" are adhered to. Attention to disciplined drug development will increase the likelihood of success, decrease the risks associated with AD drug development, enhance the ability to attract investment, and make it more likely that new therapies will become available to those with or vulnerable to the emergence of AD.

The application of positron emission tomography (PET) imaging in CNS drug development

As drug discovery and development in Neuroscience push beyond symptom management to disease modification, neuroimaging becomes a key area of translational research that enables measurements of the presence of drugs and downstream physiological consequences of drug action within the living brain. As such, neuroimaging can be used to help optimize decision-making processes throughout the various phases of drug development. Positron Emission Tomography (PET) is a functional imaging technique that allows the quantification and visualization of biochemical processes, by monitoring the time dependent distribution of molecules labelled with short-lived positron-emitting isotopes. This review focuses on the application of PET to support CNS drug development, particularly in the early clinical phases, by allowing us to measure tissue exposure, target engagement, and pharmacological activity. We will also discuss the application of PET imaging as tools to image the pathological hallmarks of disease and evaluate the potential disease-modifying effect of candidate drugs in slowing disease progression.

Identifying potential drug targets in hepatocellular carcinoma based on network analysis and one-class support vector machine

Hepatocellular carcinoma (HCC) is one major cause of cancer-related death worldwide. But now, the systematic therapy for the advanced stages of HCC is rather limited. Thus, the discovery of novel drug targets and thereafter targeted drugs against HCC is continuously needed. In this study, we combined clinical association data, gene expression profiles and manually collected drug target genes with the human protein-protein interaction (PPI) network to establish an in-silico HCC drug target predictor. First, we found drug target genes (DTGs), disease-associated genes (DAGs), prognostic unfavorable genes (PUGs) and cancer up-regulated genes (URGs) have higher degree, betweenness, closeness centrality, while cancer down-regulated genes (DRGs), prognostic favorable genes (PFGs) have lower degrees, in comparison with background genes. Moreover, DTG nodes were shown to be closer to DAG, PUG and URG nodes, but farther away from PFG and DRG nodes. Compared to the background, PFGs and DRGs were shown to have relatively bigger genetic dependency scores, while PUGs and URGs have smaller genetic dependency scores. Finally, based on the observed features of DTGs, we constructed a drug target predictor using one-class support vector machine (one-class SVM). Performance evaluation results suggested our predictor could effectively identify putative drug target genes for further research.

Phenotypes associated with genes encoding drug targets are predictive of clinical trial side effects

Only a small fraction of early drug programs progress to the market, due to safety and efficacy failures, despite extensive efforts to predict safety. Characterizing the effect of natural variation in the genes encoding drug targets should present a powerful approach to predict side effects arising from drugging particular proteins. In this retrospective analysis, we report a correlation between the organ systems affected by genetic variation in drug targets and the organ systems in which side effects are observed. Across 1819 drugs and 21 phenotype categories analyzed, drug side effects are more likely to occur in organ systems where there is genetic evidence of a link between the drug target and a phenotype involving that organ system, compared to when there is no such genetic evidence (30.0 vs 19.2%; OR = 1.80). This result suggests that human genetic data should be used to predict safety issues associated with drug targets.

Safety issues including side effects are one of the major factors causing failure of clinical trials in drug development. Here, the authors leverage information about phenotypes associated with variation in genes encoding drug targets to predict drug-treatment-related side effects.

Development of Human Target Validation Classification that Predicts Future Clinical Efficacy

Fewer new medicines have become available to patients during the last decades. Clinical efficacy failures in late-phase development have been identified as a common cause of this decline. Improved ways to ensure early selection of the right drug targets when it comes to efficacy is therefore a highly desirable goal. The aim of this work was to develop a strategy to facilitate selection of novel targets already in the discovery phase that later on in clinical development would demonstrate efficacy. A cross-functional team at AstraZeneca with extensive experience in drug discovery and development participated in several workshops to identify the critical elements that contribute to building human target validation [(HTV); the relevance of the target from a human perspective]. The elements were consolidated into a 10-point HTV classification system that was ranked from lowest to highest in terms of perceived impact on future clinical efficacy. Using 50 years of legacy research and development data, the ability of the 10-point HTV classification to predict future clinical efficacy was evaluated. Drug targets were classified as having low, medium, or high HTV at the time of candidate drug selection. Comparing this HTV classification with later clinical development efficacy data showed that HTV classification was highly predictive of future clinical efficacy success. This new strategy for HTV assessment provides a novel approach to early prediction of clinical efficacy and a better understanding of portfolio risk.

Target Identification Using Chemical Probes

Chemical probes are small molecules with potency and selectivity for a single or small number of protein targets. A good chemical probe engages its target intracellularly and is accompanied by a chemically similar, but inactive molecule to be used as a negative control in cellular phenotypic screening. The utility of these chemical probes is ultimately governed by how well they are developed and characterized. Chemical probes either as single entities, or in chemical probes sets are being increasingly used to interrogate the biological relevance of a target in a disease model. This chapter lays out the core properties of chemical probes, summarizes the seminal and emerging techniques used to demonstrate robust intracellular target engagement. Translation of target engagement assays to disease-relevant phenotypic assays using primary patient-derived cells and tissues is also reviewed. Two examples of epigenetic chemical probe discovery and utility are presented whereby target engagement pointed to novel disease associations elucidated from poorly understood protein targets. Finally, a number of examples are discussed whereby chemical probe sets, or "chemogenomic libraries" are used to illuminate new target-disease links which may represent future directions for chemical probe utility.

Phenome-wide association studies across large population cohorts support drug target validation

Phenome-wide association studies (PheWAS) have been proposed as a possible aid in drug development through elucidating mechanisms of action, identifying alternative indications, or predicting adverse drug events (ADEs). Here, we select 25 single nucleotide polymorphisms (SNPs) linked through genome-wide association studies (GWAS) to 19 candidate drug targets for common disease indications. We interrogate these SNPs by PheWAS in four large cohorts with extensive health information (23andMe, UK Biobank, FINRISK, CHOP) for association with 1683 binary endpoints in up to 697,815 individuals and conduct meta-analyses for 145 mapped disease endpoints. Our analyses replicate 75% of known GWAS associations (P < 0.05) and identify nine study-wide significant novel associations (of 71 with FDR < 0.1). We describe associations that may predict ADEs, e.g., acne, high cholesterol, gout, and gallstones with rs738409 (p.I148M) in PNPLA3 and asthma with rs1990760 (p.T946A) in IFIH1. Our results demonstrate PheWAS as a powerful addition to the toolkit for drug discovery.

Systemic administration of epothilone D improves functional recovery of walking after rat spinal cord contusion injury

Central nervous system (CNS) injuries cause permanent impairments of sensorimotor functions as mature neurons fail to regenerate their severed axons. The poor intrinsic growth capacity of adult CNS neurons and the formation of an inhibitory lesion scar are key impediments to axon regeneration. Systemic administration of the microtubule stabilizing agent epothilone B promotes axon regeneration and recovery of motor function by activating the intrinsic axonal growth machinery and by reducing the inhibitory fibrotic lesion scar. Thus, epothilones hold clinical promise as potential therapeutics for spinal cord injury. Here we tested the efficacy of epothilone D, an epothilone B analog with a superior safety profile. By using liquid chromatography and mass spectrometry (LC/MS), we found adequate CNS penetration and distribution of epothilone D after systemic administration, confirming the suitability of the drug for non-invasive CNS treatment. Systemic administration of epothilone D reduced inhibitory fibrotic scarring, promoted regrowth of injured raphespinal fibers and improved walking function after mid-thoracic spinal cord contusion injury in adult rats. These results confirm that systemic administration of epothilones is a valuable therapeutic strategy for CNS regeneration and repair after injury and provides a further advance for potential clinical translation.

Combining Zebrafish and CRISPR/Cas9: Toward a More Efficient Drug Discovery Pipeline

The use of zebrafish larvae in basic and applied research has grown exponentially during the last 20 years. The reasons for this success lay in its specific experimental advantages: on the one hand, the small size, the large number of progeny and the fast life cycle greatly facilitate large-scale approaches while maintaining 3Rs amenability; on the other hand, high genetic and physiological homology with humans and ease of genetic manipulation make zebrafish larvae a highly robust model for understanding human disease. Together, these advantages allow using zebrafish larvae for performing high-throughput research, both in terms of chemical and genetic phenotypic screenings. Therefore, the zebrafish larva as an animal model is placed between more reductionist in vitro high-throughput screenings and informative but low-throughput preclinical assays using mammals. However, despite its biological advantages and growing translational validation, zebrafish remains scarcely used in current drug discovery pipelines. In a context in which the pharmaceutical industry is facing a productivity crisis in bringing new drugs to the market, the combined advantages of zebrafish and the CRISPR/Cas9 system, the most powerful technology for genomic editing to date, has the potential to become a valuable tool for streamlining the generation of models mimicking human disease, the validation of novel drug targets and the discovery of new therapeutics. This review will focus on the most recent advances on CRISPR/Cas9 implementation in zebrafish and all their potential uses in biomedical research and drug discovery.

Genetic-Driven Druggable Target Identification and Validation

Choosing the right biological target is the critical primary decision for the development of new drugs. Systematic genetic association testing of both human diseases and quantitative traits, along with resultant findings of coincident associations between them, is becoming a powerful approach to infer drug targetable candidates and generate in vitro tests to identify compounds that can modulate them therapeutically. Here, we discuss opportunities and challenges, and infer criteria for the optimal use of genetic findings in the drug discovery pipeline.

Connecting genetics and gene expression data for target prioritisation and drug repositioning

Developing new drugs continues to be a highly inefficient and costly business. By repurposing an existing compound for a different indication, drug repositioning offers an attractive alternative to traditional drug discovery. Most of these approaches work by matching transcriptional disease signatures to anti-correlated gene expression profiles of drug perturbations. Genome-wide association studies (GWASs) are of great interest to researchers in the pharmaceutical industry because drug programmes with supporting genetic evidence are more likely to successfully progress through the drug discovery pipeline. Here, we present a systematic approach to generate drug repositioning hypothesis based on disease genetics by mining public repositories of GWAS data and drug transcriptomic profiles. We find that genes genetically associated with a certain disease are more likely to be differentially expressed in the same disease (p-value = 1.54e-17 and AUC = 0.75) and that, in existing drug - disease combinations, genes significantly up- or down-regulated after drug treatment are enriched for genes genetically associated with that disease (p-value = 1.1e-79 and AUC = 0.64). Finally, we use this framework to generate and rank novel GWAS-driven drug repositioning predictions.

High throughput scaffold-based 3D micro-tumor array for efficient drug screening and chemosensitivity testing

Oncology drug development is greatly hampered by inefficient drug screening using 2D culture. Herein, we present ready-to-use micro-scaffolds in 384-well format to generate uniform 3D micro-tumor array (3D-MTA, CV < 0.15) that predicts in vivo drug responses more accurately than 2D monolayer. 3D-MTA generated from both cell lines and primary cells achieved high screen quality (Z' > 0.5), and were compatible with standard high throughput and high content instruments. Doxorubicin identified by 3D-MTA and 2D successfully inhibited tumor growth in mice bearing lung cancer cell line (H226) xenografts, but not gemcitabine and vinorelbine, which were selected solely by 2D. Resistance towards targeted therapy was modeled on 3D-MTA, which elicited SK-BR-3 to express higher proliferation-related genes in response to gefitinb, as compared to 2D. Screening of 56 MAPK inhibitors identified pisamertib to synergistically improve cytotoxicity effect in combination with gefitinib. Primary tumor cells derived from patient-derived xenografts further attested concordance of drug response in 3D-MTA with in vivo response. 3D-MTA was further extended to realize chemosensitivity testing using patient-derived cells. Overall, 3D-MTA demonstrated strong potential to accelerate drug discovery and improve cancer treatment by providing efficient drug screening.