When Quality Beats Quantity: Decision Theory, Drug Discovery, and the Reproducibility Crisis

On-chip 3D potency assay for prediction of clinical outcomes for cell therapy candidates for osteoarthritis

The lack of clinically predictive potency assays for cell products significantly impedes translation of these therapies. Here, we describe a microfluidic on-chip 3D system for rapid evaluation of a subset of patient-derived bone marrow aspirate concentrate (BMAC) samples used in a phase 3 multicenter trial (NCT03818737) evaluating autologous cells for relieving knee osteoarthritis pain. BMAC clinical samples cultured in the on-chip 3D system exhibit elevated levels of immunomodulatory and trophic proteins compared to 2D culture. Using analyte information from in vitro assays and patient-matched clinical data, we build linear regression prediction models for clinical outcomes. We demonstrate improved clinical prediction by cross-validation accuracy for the on-chip 3D platform compared to 2D culture. Additionally, on-chip 3D assay metrics display higher correlative power with patient pain scores compared to the 2D assay. This study establishes a potency assay with improved prediction power to accelerate translation of cell therapies.

Every Compound a Candidate: experience-led risk-taking approaches to accelerate small-molecule drug discovery

Despite progress, small-molecule drug discovery remains slow and costly. A paradigm shift is underway by leveraging artificial intelligence (AI) and machine learning (ML); however, these technological advances are necessary but not sufficient. Performance indicators from our partnered portfolio include timelines for data turnaround (5-day) and candidate delivery (2.9 versus 4.0 years for industry). Together with optimised processes and effective decision-making, improved translational predictivity is required. Progressing more compounds through downstream in vitro and in vivo models will rapidly reveal translational thresholds or crucial blockers for compound progression, with humans and machines actively learning from such data. We advocate for more experience-led risk-taking and a mindset shift toward an Every Compound a Candidate strategy, which aims to deliver drug candidates in <2 years.

Shear-Stress Initiates Signal Two of NLRP3 Inflammasome Activation in LPS-Primed Macrophages through Piezo1

The innate immune system is tightly regulated by a complex network of chemical signals triggered by pathogens, cellular damage, and environmental stimuli. While it is well-established that changes in the extracellular environment can significantly influence the immune response to pathogens and damage-associated molecules, there remains a limited understanding of how changes in environmental stimuli specifically impact the activation of the NLRP3 inflammasome, a key component of innate immunity. Here, we demonstrated how shear stress can act as Signal 2 in the NLRP3 inflammasome activation pathway by treating LPS-primed immortalized bone marrow-derived macrophages (iBMDMs) with several physiologically relevant magnitudes of shear stress to induce inflammasome activation. We demonstrated that magnitudes of shear stress within 1.0 to 50 dyn/cm2 were able to induce ASC speck formation, while 50 dyn/cm2 was sufficient to induce significant calcium signaling, gasdermin-D cleavage, caspase-1 activity, and IL-1β secretion, all hallmarks of inflammasome activation. Utilizing NLRP3 and caspase-1 knockout iBMDMs, we demonstrated that the NLRP3 inflammasome was primarily activated as a result of shear stress exposure. Quantitative polymerase chain reaction (qPCR), ELISA, and a small molecule inhibitor study aided us in demonstrating that expression of Piezo1, NLRP3, gasdermin-D, IL-1β, and CCL2 secretion were all upregulated in iBMDMs treated with shear stress. This study provides a foundation for further understanding the interconnected pathogenesis of chronic inflammatory diseases and the ability of shear stress to play a role in their progression.

The latest progress in assay development in leishmaniasis drug discovery: a review of the available papers on PubMed from the past year

INTRODUCTION

Leishmaniasis is a significant neglected tropical disease with limited treatment options that urgently requires ongoing efforts in drug discovery. Recent advances have focused on the development of new assays and methods to identify effective therapeutic candidates.

AREAS COVERED

This review explores recent trends and methodologies in leishmaniasis drug discovery, with a particular focus on in silico and in vitro studies, as well as in vivo validation, using animal models. A detailed analysis of recent studies was provided, discussing the methodologies employed, such as manual and automated parasite quantification, and the use of fluorescence and luminescence-based techniques. Additionally, global research trends were analyzed, highlighting the leading countries in scientific output and the collaborative efforts driving advancements in this field.

EXPERT OPINION

The field of leishmaniasis drug discovery has rapidly progressed in the last years, but the lack of standardized methodologies and limited in vivo validation remain significant hurdles. To advance promising treatments to clinical trials, cross-validation of preclinical findings and interdisciplinary collaboration are essential. Increased funding and global partnerships are also crucial to accelerate the discovery and development of alternative and effective therapies.

Cell type transcriptional identities are maintained in cultured ex vivo human brain tissue

It is becoming more broadly accepted that human-based models are needed to better understand the complexities of the human nervous system and its diseases. The recently developed human brain organotypic culture model is one highly promising model that requires the involvement of neurosurgeons and neurosurgical patients. Studies have investigated the electrophysiological properties of neurons in such ex vivo human tissues, but the maintenance of other cell types within explanted brain remains largely unknown. Here, using single-nucleus RNA sequencing, we systematically evaluate the transcriptional identities of the various cell types found in six patient samples after fourteen days in culture (83,501 nuclei from day 0 samples and 45,738 nuclei from day 14 samples). We used two pediatric temporal lobectomy samples, an adult frontal cortex sample, two IDH wild-type glioblastoma samples, and one medulloblastoma sample. We found remarkably high correlations of day 14 transcriptional identities to day 0 tissue, especially in tumor cells (r = 0.90 to 0.93), though microglia (r = 0.86), oligodendrocytes (r = 0.80), pericytes (r = 0.77), endothelial cells (r = 0.78), and fibroblasts (r = 0.76) showed strong preservation of their transcriptional profiles as well. Astrocytes and excitatory neurons showed more moderate preservation (r = 0.66 and 0.47, respectively). Because the main difficulty with organotypic brain cultures is the acquisition of human tissue, which is readily available to neurosurgeons, this model is easily accessible to neurosurgeon-scientists and neurosurgeons affiliated with research laboratories. Broad uptake of this more representative model should prompt advances in our understanding of many uniquely human diseases, lead to more reliable clinical trial performance, and ultimately yield better therapies for our patients.

Common AAV gene therapy vectors show indiscriminate transduction of living human brain cell types

The development of cell-type-specific gene therapy vectors for treating neurological diseases holds great promise, but has relied on animal models with limited translational utility. We have adapted an ex vivo organotypic model to evaluate adeno-associated virus (AAV) transduction properties in living slices of human brain tissue. Using fluorescent reporter expression and single-nucleus RNA sequencing, we found that common AAV vectors show broad transduction of normal cell types, with protein expression most apparent in astrocytes; this work introduces a pipeline for identifying and optimizing AAV gene therapy vectors in human brain samples.

Neuromodulatory effects of leukotriene receptor antagonists: A comprehensive review

Cysteinyl leukotrienes (CysLTs) are central to the pathophysiology of asthma and various inflammatory disorders. Leukotriene receptor antagonists (LTRAs) effectively treat respiratory conditions by targeting cysteinyl leukotriene receptors, CysLT1 and CysLT2 subtypes. This review explores the multifaceted effects of LTs, extending beyond bronchoconstriction. CysLT receptors are not only present in the respiratory system but are also crucial in neuronal signaling pathways. LTRAs modulate these receptors, influencing downstream signaling, calcium levels, inflammation, and oxidative stress (OS) within neurons hinting at broader implications. Recent studies identify novel molecular targets, sparking interest in repurposing LTRAs for therapeutic use. Clinical trials are investigating their potential in neuroinflammation control, particularly in Alzheimer's disease (AD) and Parkinson's diseases (PD). However, montelukast, a long-standing LTRA since 1998, raises concerns due to neuropsychiatric adverse drug reactions (ADRs). Despite widespread use, understanding montelukast's metabolism and underlying ADR mechanisms remains limited. This review comprehensively examines LTRAs' diverse biological effects, emphasizing non-bronchoconstrictive activities. It also analyses plausible mechanisms behind LTRAs' neuronal effects, offering insights into their potential as neurodegenerative disease modulators. The aim is to inform clinicians, researchers, and pharmaceutical developers about LTRAs' expanding roles, particularly in neuroinflammation control and their promising repurposing for neurodegenerative disease management.

The Art of Finding the Right Drug Target: Emerging Methods and Strategies

Drug targets are specific molecules in biological tissues and body fluids that interact with drugs. Drug target discovery is a key component of drug discovery and is essential for the development of new drugs in areas such as cancer therapy and precision medicine. Traditional in vitro or in vivo target discovery methods are time-consuming and labor-intensive, limiting the pace of drug discovery. With the development of modern discovery methods, the discovery and application of various emerging technologies have greatly improved the efficiency of drug discovery, shortened the cycle time, and reduced the cost. This review provides a comprehensive overview of various emerging drug target discovery strategies, including computer-assisted approaches, drug affinity response target stability, multiomics analysis, gene editing, and nonsense-mediated mRNA degradation, and discusses the effectiveness and limitations of the various approaches, as well as their application in real cases. Through the review of the aforementioned contents, a general overview of the development of novel drug targets and disease treatment strategies will be provided, and a theoretical basis will be provided for those who are engaged in pharmaceutical science research. SIGNIFICANCE STATEMENT: Target-based drug discovery has been the main approach to drug discovery in the pharmaceutical industry for the past three decades. Traditional drug target discovery methods based on in vivo or in vitro validation are time-consuming and costly, greatly limiting the development of new drugs. Therefore, the development and selection of new methods in the drug target discovery process is crucial.

Evolution of Drug Development and Regulatory Affairs: The Demonstrated Power of Artificial Intelligence

PURPOSE

Artificial intelligence (AI) refers to technology capable of mimicking human cognitive functions and has important applications across all sectors and industries, including drug development. This has considerable implications for the regulation of drug development processes, as it is expected to transform both the way drugs are brought to market and the systems through which this process is controlled. There is currently insufficient evidence in published literature of the real-world applications of AI. Therefore, this narrative review investigated, collated, and elucidated the applications of AI in drug development and its regulatory processes.

METHODS

A narrative review was conducted to ascertain the role of AI in streamlining drug development and regulatory processes.

FINDINGS

The findings of this review revealed that machine learning or deep learning, natural language processing, and robotic process automation were favored applications of AI. Each of them had considerable implications on the operations they were intended to support. Overall, the AI tools facilitated access and provided manageability of information for decision-making across the drug development lifecycle. However, the findings also indicate that additional work is required by regulatory authorities to set out appropriate guidance on applications of the technology, which has critical implications for safety, regulatory process workflow and product development costs.

IMPLICATIONS

AI has adequately proven its utility in drug development, prompting further investigations into the translational value of its utility based on cost and time saved for the delivery of essential drugs.

Bridging the gap between target-based and phenotypic-based drug discovery

INTRODUCTION

The unparalleled progress in science of the last decades has brought a better understanding of the molecular mechanisms of diseases. This promoted drug discovery processes based on a target approach. However, despite the high promises associated, a critical decrease in the number of first-in-class drugs has been observed.

AREAS COVERED

This review analyses the challenges, advances, and opportunities associated with the main strategies of the drug discovery process, i.e. based on a rational target approach and on an empirical phenotypic approach. This review also evaluates how the gap between these two crossroads can be bridged toward a more efficient drug discovery process.

EXPERT OPINION

The critical lack of knowledge of the complex biological networks is leading to targets not relevant for the clinical context or to drugs that present undesired adverse effects. The phenotypic systems designed by considering available molecular mechanisms can mitigate these knowledge gaps. Associated with the expansion of the chemical space and other technologies, these designs can lead to more efficient drug discoveries. Technological and scientific knowledge should also be applied to identify, as early as possible, both drug targets and mechanisms of action, leading to a more efficient drug discovery pipeline.

DrugGym: A testbed for the economics of autonomous drug discovery

Drug discovery is stochastic. The effectiveness of candidate compounds in satisfying design objectives is unknown ahead of time, and the tools used for prioritization-predictive models and assays-are inaccurate and noisy. In a typical discovery campaign, thousands of compounds may be synthesized and tested before design objectives are achieved, with many others ideated but deprioritized. These challenges are well-documented, but assessing potential remedies has been difficult. We introduce DrugGym, a framework for modeling the stochastic process of drug discovery. Emulating biochemical assays with realistic surrogate models, we simulate the progression from weak hits to sub-micromolar leads with viable ADME. We use this testbed to examine how different ideation, scoring, and decision-making strategies impact statistical measures of utility, such as the probability of program success within predefined budgets and the expected costs to achieve target candidate profile (TCP) goals. We also assess the influence of affinity model inaccuracy, chemical creativity, batch size, and multi-step reasoning. Our findings suggest that reducing affinity model inaccuracy from 2 to 0.5 pIC50 units improves budget-constrained success rates tenfold. DrugGym represents a realistic testbed for machine learning methods applied to the hit-to-lead phase. Source code is available at www.drug-gym.org.

Frameworks for transformational breakthroughs in RNA-based medicines

RNA has sparked a revolution in modern medicine, with the potential to transform the way we treat diseases. Recent regulatory approvals, hundreds of new clinical trials, the emergence of CRISPR gene editing, and the effectiveness of mRNA vaccines in dramatic response to the COVID-19 pandemic have converged to create tremendous momentum and expectation. However, challenges with this relatively new class of drugs persist and require specialized knowledge and expertise to overcome. This Review explores shared strategies for developing RNA drug platforms, including layering technologies, addressing common biases and identifying gaps in understanding. It discusses the potential of RNA-based therapeutics to transform medicine, as well as the challenges associated with improving applicability, efficacy and safety profiles. Insights gained from RNA modalities such as antisense oligonucleotides (ASOs) and small interfering RNAs are used to identify important next steps for mRNA and gene editing technologies.

Making good decisions in early drug discovery

The earliest phases of drug discovery require diverse scientific disciplines to work hand in hand to address many unknowns. Good decision making is crucial for success in this context and, yet, the topic of sound planning has rarely been addressed for the earliest stages of drug discovery. We propose a tailored, qualitative 'decision quality' process that can serve as a guide toward generating project plans optimized to address a given project situation. Furthermore, we propose a visual flow-chart format for the selected plan that includes key decisions and activities, together forming a decision roadmap of the plan. We illustrate each step of the process by means of a real-life example and provide recommendations for its implementation.

FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology

In the realm of medicinal chemistry, the primary objective is to swiftly optimize a multitude of chemical properties of a set of compounds to yield a clinical candidate poised for clinical trials. In recent years, two computational techniques, machine learning (ML) and physics-based methods, have evolved substantially and are now frequently incorporated into the medicinal chemist's toolbox to enhance the efficiency of both hit optimization and candidate design. Both computational methods come with their own set of limitations, and they are often used independently of each other. ML's capability to screen extensive compound libraries expediently is tempered by its reliance on quality data, which can be scarce especially during early-stage optimization. Contrarily, physics-based approaches like free energy perturbation (FEP) are frequently constrained by low throughput and high cost by comparison; however, physics-based methods are capable of making highly accurate binding affinity predictions. In this study, we harnessed the strength of FEP to overcome data paucity in ML by generating virtual activity data sets which then inform the training of algorithms. Here, we show that ML algorithms trained with an FEP-augmented data set could achieve comparable predictive accuracy to data sets trained on experimental data from biological assays. Throughout the paper, we emphasize key mechanistic considerations that must be taken into account when aiming to augment data sets and lay the groundwork for successful implementation. Ultimately, the study advocates for the synergy of physics-based methods and ML to expedite the lead optimization process. We believe that the physics-based augmentation of ML will significantly benefit drug discovery, as these techniques continue to evolve.

Testing of putative antiseizure medications in a preclinical Dravet syndrome zebrafish model

Dravet syndrome is a severe genetic epilepsy primarily caused by de novo mutations in a voltage-activated sodium channel gene (SCN1A). Patients face life-threatening seizures that are largely resistant to available anti-seizure medications. Preclinical Dravet syndrome animal models are a valuable tool to identify candidate anti-seizure medications for these patients. Among these, scn1lab mutant zebrafish, exhibiting spontaneous seizure-like activity, are particularly amenable to large-scale drug screening. Thus far, we have screened more than 3000 drug candidates in scn1lab zebrafish mutants, identifying valproate, stiripentol, and fenfluramine e.g. Food and Drug Administration-approved drugs, with clinical application in the Dravet syndrome population. Successful phenotypic screening in scn1lab mutant zebrafish is rigorous and consists of two stages: (i) a locomotion-based assay measuring high-velocity convulsive swim behaviour and (ii) an electrophysiology-based assay, using in vivo local field potential recordings, to quantify electrographic seizure-like events. Historically, nearly 90% of drug candidates fail during translation from preclinical models to the clinic. With such a high failure rate, it becomes necessary to address issues of replication and false positive identification. Leveraging our scn1lab zebrafish assays is one approach to address these problems. Here, we curated a list of nine anti-seizure drug candidates recently identified by other groups using preclinical Dravet syndrome models: 1-Ethyl-2-benzimidazolinone, AA43279, chlorzoxazone, donepezil, lisuride, mifepristone, pargyline, soticlestat and vorinostat. First-stage locomotion-based assays in scn1lab mutant zebrafish identified only 1-Ethyl-2-benzimidazolinone, chlorzoxazone and lisuride. However, second-stage local field potential recording assays did not show significant suppression of spontaneous electrographic seizure activity for any of the nine anti-seizure drug candidates. Surprisingly, soticlestat induced frank electrographic seizure-like discharges in wild-type control zebrafish. Taken together, our results failed to replicate clear anti-seizure efficacy for these drug candidates highlighting a necessity for strict scientific standards in preclinical identification of anti-seizure medications.

Complex or not too complex? One size does not fit all in next generation microphysiological systems

In the attempt to overcome the increasingly recognized shortcomings of existing in vitro and in vivo models, researchers have started to implement alternative models, including microphysiological systems. First examples were represented by 2.5D systems, such as microfluidic channels covered by cell monolayers as blood vessel replicates. In recent years, increasingly complex microphysiological systems have been developed, up to multi-organ on chip systems, connecting different 3D tissues in the same device. However, such an increase in model complexity raises several questions about their exploitation and implementation into industrial and clinical applications, ranging from how to improve their reproducibility, robustness, and reliability to how to meaningfully and efficiently analyze the huge amount of heterogeneous datasets emerging from these devices. Considering the multitude of envisaged applications for microphysiological systems, it appears now necessary to tailor their complexity on the intended purpose, being academic or industrial, and possibly combine results deriving from differently complex stages to increase their predictive power.

Advancements in the use of xenopus oocytes for modelling neurological disease for novel drug discovery

INTRODUCTION

Introduced about 50 years ago, the model of Xenopus oocytes for the expression of recombinant proteins has gained a broad spectrum of applications. The authors herein review the benefits brought from using this model system, with a focus on modeling neurological disease mechanisms and application to drug discovery.

AREAS COVERED

Using multiple examples spanning from ligand gated ion channels to transporters, this review presents, in the light of the latest publications, the benefits offered from using Xenopus oocytes. Studies range from the characterization of gene mutations to the discovery of novel treatments for disorders of the central nervous system (CNS).

EXPERT OPINION

Development of new drugs targeting CNS disorders has been marked by failures in the translation from preclinical to clinical studies. As progress in genetics and molecular biology highlights large functional differences arising from a single to a few amino acid exchanges, the need for drug screening and functional testing against human proteins is increasing. The use of Xenopus oocytes to enable precise modeling and characterization of clinically relevant genetic variants constitutes a powerful model system that can be used to inform various aspects of CNS drug discovery and development.

Flow-Induced Shear Stress Primes NLRP3 Inflammasome Activation in Macrophages via Piezo1

The NLRP3 inflammasome is a crucial component of the innate immune system, playing a pivotal role in initiating and regulating the body's inflammatory response to various pathogens and cellular damage. Environmental stimuli, such as temperature, pH level, and nutrient availability, can influence the behavior and functions of innate immune cells, including immune cell activity, proliferation, and cytokine production. However, there is limited understanding regarding how mechanical forces, like shear stress, govern the intrinsic inflammatory reaction, particularly the activation of the NLRP3 inflammasome, and how shear stress impacts NLRP3 inflammasome activation through its capacity to induce alterations in gene expression and cytokine secretion. Here, we investigated how shear stress can act as a priming signal in NLRP3 inflammasome activation by exposing immortalized bone marrow-derived macrophages (iBMDMs) to numerous physiologically relevant magnitudes of shear stress before chemically inducing inflammasome activation. We demonstrated that shear stress of large magnitudes was able to prime iBMDMs more effectively for inflammasome activation compared to lower shear stress magnitudes, as quantified by the percentage of cells where ASC-CFP specks formed and IL-1β secretion, the hallmarks of inflammasome activation. Testing this in NLRP3 and caspase-1 knockout iBMDMs showed that the NLRP3 inflammasome was primarily primed for activation due to shear stress exposure. Quantitative polymerase chain reaction (qPCR) and a small-molecule inhibitor study mechanistically determined that shear stress regulates the NLRP3 inflammasome by upregulating Piezo1, IKKβ, and NLRP3. These findings offer insights into the mechanistic relationship among physiological shear stresses, inflammasome activation, and their impact on the progression of inflammatory diseases and their interconnected pathogenesis.

Electrostatically Cross-Linked Bioinks for Jetting-Based Bioprinting of 3D Cell Cultures

It has been acknowledged that thousands of drugs that passed two-dimensional (2D) cell culture models and animal studies often fail when entering human clinical trials. Despite the significant development of three-dimensional (3D) models, developing a high-throughput model that can be reproducible on a scale remains challenging. One of the main challenges is precise cell deposition and the formation of a controllable number of spheroids to achieve more reproducible results for drug discovery and treatment applications. Furthermore, when transitioning from manually generated structures to 3D bioprinted structures, the choice of material is limited due to restrictions on materials that are applicable with bioprinters. Herein, we have shown the capability of a fast-cross-linking bioink that can be used to create a single spheroid with varying diameters (660, 1100, and 1340 μm) in a high-throughput manner using a commercialized drop-on-demand bioprinter. Throughout this work, we evaluate the physical properties of printable ink with and without cells, printing optimization, cytocompatibility, cell sedimentation, and homogeneity in ink during the printing process. This work showcases the importance of ink characterization to determine printability and precise cell deposition. The knowledge gained from this work will accelerate the development of next-generation inks compatible with a drop-on-demand 3D bioprinter for various applications such as precision models to mimic diseases, toxicity tests, and the drug development process.

Systematic heterogenization revisited: Increasing variation in animal experiments to improve reproducibility?

Life sciences are currently facing a reproducibility crisis. Originally, the crisis was born out of single alarming failures to reproduce findings at different times and locations. Nowadays, systematic studies indicate that the prevalence of irreproducible research does in fact exceed 50%. Viewed from a rather cynical perspective, Fett's law of the lab "Never replicate a successful experiment" has thus taken on a completely new meaning. In this respect, animal research has come under particular scrutiny, as the stakes are high in terms of both research ethics and societal impact. To counteract this, it is essential to identify sources of poor reproducibility as well as to iron out these failures. We here review the current debate, briefly discuss potential reasons, and summarize steps that have already been undertaken to improve reproducibility in animal research. By the example of classical behavioural phenotyping studies, we particularly highlight the role strict standardization plays in exacerbating the crisis, and review the concept of systematic heterogenization as an alternative strategy to deal with variation in animal studies. Briefly, we argue that systematic variation rather than strict homogenization of experimental conditions benefits the robustness of research findings, and hence their reproducibility. To this end, we will present concrete examples for systematically heterogenized experiments and provide a practical guide on how to apply systematic heterogenization in experimental practice.

Practical approaches to improving translatability and reproducibility in preclinical pain research

Pain research continues to face the challenge of poor translatability of pre-clinical studies. In this short primer, we are summarizing the possible causes, with an emphasis on practical and constructive solutions. In particular, we stress the importance of increased heterogeneity in animal studies; formal or informal pre-registration to combat publication bias; and increased statistical training in order to help pre-clinical scientists appreciate the usefulness of available experimental design and reporting guidelines.

From intuition to AI: evolution of small molecule representations in drug discovery

Within drug discovery, the goal of AI scientists and cheminformaticians is to help identify molecular starting points that will develop into safe and efficacious drugs while reducing costs, time and failure rates. To achieve this goal, it is crucial to represent molecules in a digital format that makes them machine-readable and facilitates the accurate prediction of properties that drive decision-making. Over the years, molecular representations have evolved from intuitive and human-readable formats to bespoke numerical descriptors and fingerprints, and now to learned representations that capture patterns and salient features across vast chemical spaces. Among these, sequence-based and graph-based representations of small molecules have become highly popular. However, each approach has strengths and weaknesses across dimensions such as generality, computational cost, inversibility for generative applications and interpretability, which can be critical in informing practitioners' decisions. As the drug discovery landscape evolves, opportunities for innovation continue to emerge. These include the creation of molecular representations for high-value, low-data regimes, the distillation of broader biological and chemical knowledge into novel learned representations and the modeling of up-and-coming therapeutic modalities.

On the difficulty of validating molecular generative models realistically: a case study on public and proprietary data

While a multitude of deep generative models have recently emerged there exists no best practice for their practically relevant validation. On the one hand, novel de novo-generated molecules cannot be refuted by retrospective validation (so that this type of validation is biased); but on the other hand prospective validation is expensive and then often biased by the human selection process. In this case study, we frame retrospective validation as the ability to mimic human drug design, by answering the following question: Can a generative model trained on early-stage project compounds generate middle/late-stage compounds de novo? To this end, we used experimental data that contains the elapsed time of a synthetic expansion following hit identification from five public (where the time series was pre-processed to better reflect realistic synthetic expansions) and six in-house project datasets, and used REINVENT as a widely adopted RNN-based generative model. After splitting the dataset and training REINVENT on early-stage compounds, we found that rediscovery of middle/late-stage compounds was much higher in public projects (at 1.60%, 0.64%, and 0.21% of the top 100, 500, and 5000 scored generated compounds) than in in-house projects (where the values were 0.00%, 0.03%, and 0.04%, respectively). Similarly, average single nearest neighbour similarity between early- and middle/late-stage compounds in public projects was higher between active compounds than inactive compounds; however, for in-house projects the converse was true, which makes rediscovery (if so desired) more difficult. We hence show that the generative model recovers very few middle/late-stage compounds from real-world drug discovery projects, highlighting the fundamental difference between purely algorithmic design and drug discovery as a real-world process. Evaluating de novo compound design approaches appears, based on the current study, difficult or even impossible to do retrospectively.Scientific Contribution This contribution hence illustrates aspects of evaluating the performance of generative models in a real-world setting which have not been extensively described previously and which hopefully contribute to their further future development.

A framework for considering prior information in network-based approaches to omics data analysis

For decades, molecular biologists have been uncovering the mechanics of biological systems. Efforts to bring their findings together have led to the development of multiple databases and information systems that capture and present pathway information in a computable network format. Concurrently, the advent of modern omics technologies has empowered researchers to systematically profile cellular processes across different modalities. Numerous algorithms, methodologies, and tools have been developed to use prior knowledge networks (PKNs) in the analysis of omics datasets. Interestingly, it has been repeatedly demonstrated that the source of prior knowledge can greatly impact the results of a given analysis. For these methods to be successful it is paramount that their selection of PKNs is amenable to the data type and the computational task they aim to accomplish. Here we present a five-level framework that broadly describes network models in terms of their scope, level of detail, and ability to inform causal predictions. To contextualize this framework, we review a handful of network-based omics analysis methods at each level, while also describing the computational tasks they aim to accomplish.

Analysis of pharma R&D productivity - a new perspective needed

R&D productivity continues to be the industry's grand challenge. We analyzed the R&D input, output, and outcome of 16 leading research-based pharmaceutical companies over 20 years (2001-2020). Our analysis shows that pharma companies increased their R&D spending at a compound annual growth rate of 6% (2001-2020) to an average R&D expenditure per company of $6.7 billion (2020). The companies in our investigation launched 251 new drugs representing 46% of all CDER-related FDA approvals in the past 20 years. The average R&D efficiency of big pharma was $6.16 billion total R&D expenditures per new drug. Almost half of the leading companies needed to compensate for their negative R&D productivity through mergers and acquisitions.

Is Target-Based Drug Discovery Efficient? Discovery and "Off-Target" Mechanisms of All Drugs

Target-based drug discovery is the dominant paradigm of drug discovery; however, a comprehensive evaluation of its real-world efficiency is lacking. Here, a manual systematic review of about 32000 articles and patents dating back to 150 years ago demonstrates its apparent inefficiency. Analyzing the origins of all approved drugs reveals that, despite several decades of dominance, only 9.4% of small-molecule drugs have been discovered through "target-based" assays. Moreover, the therapeutic effects of even this minimal share cannot be solely attributed and reduced to their purported targets, as they depend on numerous off-target mechanisms unconsciously incorporated by phenotypic observations. The data suggest that reductionist target-based drug discovery may be a cause of the productivity crisis in drug discovery. An evidence-based approach to enhance efficiency seems to be prioritizing, in selecting and optimizing molecules, higher-level phenotypic observations that are closer to the sought-after therapeutic effects using tools like artificial intelligence and machine learning.

Rewiring Drug Research and Development through Human Data-Driven Discovery (HD3)

In an era of unparalleled technical advancement, the pharmaceutical industry is struggling to transform data into increased research and development efficiency, and, as a corollary, new drugs for patients. Here, we briefly review some of the commonly discussed issues around this counterintuitive innovation crisis. Looking at both industry- and science-related factors, we posit that traditional preclinical research is front-loading the development pipeline with data and drug candidates that are unlikely to succeed in patients. Applying a first principles analysis, we highlight the critical culprits and provide suggestions as to how these issues can be rectified through the pursuit of a Human Data-driven Discovery (HD3) paradigm. Consistent with other examples of disruptive innovation, we propose that new levels of success are not dependent on new inventions, but rather on the strategic integration of existing data and technology assets. In support of these suggestions, we highlight the power of HD3, through recently published proof-of-concept applications in the areas of drug safety analysis and prediction, drug repositioning, the rational design of combination therapies and the global response to the COVID-19 pandemic. We conclude that innovators must play a key role in expediting the path to a largely human-focused, systems-based approach to drug discovery and research.

Cellular signaling pathways as plastic, proto-cognitive systems: Implications for biomedicine

Many aspects of health and disease are modeled using the abstraction of a "pathway"-a set of protein or other subcellular activities with specified functional linkages between them. This metaphor is a paradigmatic case of a deterministic, mechanistic framework that focuses biomedical intervention strategies on altering the members of this network or the up-/down-regulation links between them-rewiring the molecular hardware. However, protein pathways and transcriptional networks exhibit interesting and unexpected capabilities such as trainability (memory) and information processing in a context-sensitive manner. Specifically, they may be amenable to manipulation via their history of stimuli (equivalent to experiences in behavioral science). If true, this would enable a new class of biomedical interventions that target aspects of the dynamic physiological "software" implemented by pathways and gene-regulatory networks. Here, we briefly review clinical and laboratory data that show how high-level cognitive inputs and mechanistic pathway modulation interact to determine outcomes in vivo. Further, we propose an expanded view of pathways from the perspective of basal cognition and argue that a broader understanding of pathways and how they process contextual information across scales will catalyze progress in many areas of physiology and neurobiology. We argue that this fuller understanding of the functionality and tractability of pathways must go beyond a focus on the mechanistic details of protein and drug structure to encompass their physiological history as well as their embedding within higher levels of organization in the organism, with numerous implications for data science addressing health and disease. Exploiting tools and concepts from behavioral and cognitive sciences to explore a proto-cognitive metaphor for the pathways underlying health and disease is more than a philosophical stance on biochemical processes; at stake is a new roadmap for overcoming the limitations of today's pharmacological strategies and for inferring future therapeutic interventions for a wide range of disease states.

Assessing the publishing priorities and preferences among STEM researchers at a large R1 institution

The cost of academic publishing has increased substantially despite the ease with which information can be shared on the web. Open Access publishing is a key mechanism for amplifying research access, inclusivity, and impact. Despite this, shifting to a free-to-read publishing environment requires navigating complex barriers that vary by career status and publishing expectations. In this article, we investigate the motivations and preferences of researchers situated within our large research institution as a case study for publishing attitudes at similar institutions. We surveyed the publishing priorities and preferences of researchers at various career stages in STEM fields as they relate to openness, data practices, and assessment of research impact. Our results indicate that publishing preferences, data management experience and research impact assessment vary by career status and departmental approaches to promotion. We find that open access publishing is widely appreciated regardless of career status, but financial limitations and publishing expectations were common barriers to publishing in Open Access journals. Our findings shed light on publishing attitudes and preferences among researchers at a major R1 research institution, and offer insight into advocacy strategies that incentivize open access publishing.

Is Cancer Reversible? Rethinking Carcinogenesis Models-A New Epistemological Tool

A growing number of studies shows that it is possible to induce a phenotypic transformation of cancer cells from malignant to benign. This process is currently known as "tumor reversion". However, the concept of reversibility hardly fits the current cancer models, according to which gene mutations are considered the primary cause of cancer. Indeed, if gene mutations are causative carcinogenic factors, and if gene mutations are irreversible, how long should cancer be considered as an irreversible process? In fact, there is some evidence that intrinsic plasticity of cancerous cells may be therapeutically exploited to promote a phenotypic reprogramming, both in vitro and in vivo. Not only are studies on tumor reversion highlighting a new, exciting research approach, but they are also pushing science to look for new epistemological tools capable of better modeling cancer.

Using Transcriptomics and Cell Morphology Data in Drug Discovery: The Long Road to Practice

Gene expression and cell morphology data are high-dimensional biological readouts of much recent interest for drug discovery. They are able to describe biological systems in different states (e.g., healthy and diseased), as well as biological systems before and after compound treatment, and they are hence useful for matching both spaces (e.g., for drug repurposing) as well as for characterizing compounds with respect to efficacy and safety endpoints. This Microperspective describes recent advances in this direction with a focus on applied drug discovery and drug repurposing, as well as outlining what else is needed to advance further, with a particular focus on better understanding the applicability domain of readouts and their relevance for decision making, which is currently often still unclear.

Drug discovery for parasitic diseases: powered by technology, enabled by pharmacology, informed by clinical science

While prevention is a bedrock of public health, innovative therapeutics are needed to complement the armamentarium of interventions required to achieve disease control and elimination targets for neglected diseases. Extraordinary advances in drug discovery technologies have occurred over the past decades, along with accumulation of scientific knowledge and experience in pharmacological and clinical sciences that are transforming many aspects of drug R&D across disciplines. We reflect on how these advances have propelled drug discovery for parasitic infections, focusing on malaria, kinetoplastid diseases, and cryptosporidiosis. We also discuss challenges and research priorities to accelerate discovery and development of urgently needed novel antiparasitic drugs.

Molecular Neurosurgery: Introduction to Gene Therapy and Clinical Applications

To date, more than 100 clinical trials have used sequence-based therapies to address diseases of the pediatric central nervous system. The first targeted pathologies share common features: the diseases are severe; they are due (mostly) to single variants; the variants are well characterized within the genome; and the interventions are technically feasible. Interventions range from intramuscular and intravenous injection to intrathecal and intraparenchymal infusions. Whether the therapeutic sequence consists of RNA or DNA, and whether the sequence is delivered via simple oligonucleotide, nanoparticle, or viral vector depends on the disease and the involved cell type(s) of the nervous system. While only one active trial targets an epilepsy disorder—Dravet syndrome—experiences with aromatic L-amino acid decarboxylase deficiency, spinal muscular atrophy, and others have taught us several lessons that will undoubtedly apply to the future of gene therapy for epilepsies. Epilepsies, with their diverse underlying mechanisms, will have unique aspects that may influence gene therapy strategies, such as targeting the epileptic zone or nodes in affected circuits, or alternatively finding ways to target nearly every neuron in the brain. This article focuses on the current state of gene therapy and includes its history and premise, the strategy and delivery vehicles most commonly used, and details viral vectors, current trials, and considerations for the future of pediatric intracranial gene therapy.

Novel hydrogels: are they poised to transform 3D cell-based assay systems in early drug discovery?

INTRODUCTION

Success in drug discovery remains unpredictable. However, more predictive and relevant disease models are becoming pivotal to demonstrating the clinical benefits of new drugs earlier in the lengthy drug discovery process. Novel hydrogel scaffolds are being developed to transform the relevance of such 3D cell-based in vitro assay systems.

AREAS COVERED

Most traditional hydrogels are still of unknown composition and suffer significant batch-to-batch variations, which lead to technical constraints. This article looks at how a new generation of novel synthetic hydrogels that are based on self-assembling peptides are poised to transform 3D cell-based assay systems by improving their relevance, reproducibility and scalability.

EXPERT OPINION

The emerging advantages of using these novel hydrogels for human 3D screening assays should enable the discovery of more cost-effective drugs, leading to improved patient benefits. Such a disruptive change could also reduce the considerable time lag from obtaining in vitro assay data to initiating clinical trials. There is now a sufficient body of data available in the literature to enable this ambition to become a reality by significantly improving the predictive validity of 3D cell-based assays in early drug discovery. Novel hydrogels are key to unlocking the full potential of these assay systems.

Performance assessment and economic analysis of a human Liver-Chip for predictive toxicology

BACKGROUND

Conventional preclinical models often miss drug toxicities, meaning the harm these drugs pose to humans is only realized in clinical trials or when they make it to market. This has caused the pharmaceutical industry to waste considerable time and resources developing drugs destined to fail. Organ-on-a-Chip technology has the potential improve success in drug development pipelines, as it can recapitulate organ-level pathophysiology and clinical responses; however, systematic and quantitative evaluations of Organ-Chips' predictive value have not yet been reported.

METHODS

870 Liver-Chips were analyzed to determine their ability to predict drug-induced liver injury caused by small molecules identified as benchmarks by the Innovation and Quality consortium, who has published guidelines defining criteria for qualifying preclinical models. An economic analysis was also performed to measure the value Liver-Chips could offer if they were broadly adopted in supporting toxicity-related decisions as part of preclinical development workflows.

RESULTS

Here, we show that the Liver-Chip met the qualification guidelines across a blinded set of 27 known hepatotoxic and non-toxic drugs with a sensitivity of 87% and a specificity of 100%. We also show that this level of performance could generate over $3 billion annually for the pharmaceutical industry through increased small-molecule R&D productivity.

CONCLUSIONS

The results of this study show how incorporating predictive Organ-Chips into drug development workflows could substantially improve drug discovery and development, allowing manufacturers to bring safer, more effective medicines to market in less time and at lower costs.

Repurposing drugs to treat cardiovascular disease in the era of precision medicine

Drug repurposing is the use of a given therapeutic agent for indications other than that for which it was originally designed or intended. The concept is appealing because of potentially lower development costs and shorter timelines than are needed to produce a new drug. To date, drug repurposing for cardiovascular indications has been opportunistic and driven by knowledge of disease mechanisms or serendipitous observation rather than by systematic endeavours to match an existing drug to a new indication. Innovations in two areas of personalized medicine - computational approaches to associate drug effects with disease signatures and predictive model systems to screen drugs for disease-modifying activities - support efforts that together create an efficient pipeline to systematically repurpose drugs to treat cardiovascular disease. Furthermore, new experimental strategies that guide the medicinal chemistry re-engineering of drugs could improve repurposing efforts by tailoring a medicine to its new indication. In this Review, we summarize the historical approach to repurposing and discuss the technological advances that have created a new landscape of opportunities.

Biopharmaceutical R&D outsourcing: Short-term gain for long-term pain?

Research and development (R&D) outsourcing offers some obvious productivity benefits (e.g., access to new technology, variabilised costs, risk sharing, etc.). However, recent work in economics points to a productivity headwind at the level of the innovation ecosystem. The market for technologies with economies of scope and knowledge spillovers (those with the biggest impact on industry economics and social welfare) has structural features that allow customers to capture a disproportionate share of economic value and transfer a disproportionate share of economic risk to technology providers, even though the providers aim to maximise profit. This reduces the incentives to invest in new ventures that specialise in the most promising early-stage projects. Therefore, near-term gains from R&D outsourcing can be offset by slower innovation in the long run.

The discovery of novel immunomodulatory medicinal plants by combination of historical text reviews and immunological screening assays

ETHNOPHARMACOLOGICAL RELEVANCE

With the advent of immunotherapies against cancers, autoimmune diseases and infections, there is a steady demand for novel medicines. New sources for discovery of potentially novel immunomodulatory compounds are therefore needed. Nature contains a large and diverse reservoir of novel compounds that can be exploited for their potential as new drugs, and exploring the pharmaceutical potential of medicinal plants used in traditional medicine is highly relevant.

AIM OF THE STUDY

We aimed with this study to explore usage of medicinal plants in Scandinavian folk medicine against diseases interpreted to involve the immune system, and to further screen water extracts from previously overlooked medicinal plants in order to discover potential new sources of immunomodulatory compounds.

MATERIALS AND METHODS

We systematically investigated historical records dating back to the 1800s with an emphasis on plants used as treatment for wounds or diseases interpreted to be inflammatory. Of 74 candidate plants, 23 pharmacologically under-studied species were selected for further characterization. The plants were collected from their natural habitats in Southern Norway, air-dried, and subjected to boiling water and accelerated solvent extraction. The crude extracts were separated into polysaccharide-enriched fractions and C-18 solid phase extracted fractions. Immunological screenings were performed with all extracts and fractions. Monosaccharide composition and total phenolic content were determined and compared across all species.

RESULTS

We identified 10 species with clear immune activating effects and 8 species with immune inhibitory effects by comparing cytokine production by human peripheral blood mononuclear cells, primary human T- and NK-cell proliferation, and nitric oxide production from macrophages.

CONCLUSIONS

With this study, we provide a comprehensive overview of Scandinavian medicinal plants and their usage, and our findings support an approach of combining historical sources with modern pharmacology in the discovery of plant sources containing potentially new pharmacological compounds.

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.

DOCKSTRING: Easy Molecular Docking Yields Better Benchmarks for Ligand Design

The field of machine learning for drug discovery is witnessing an explosion of novel methods. These methods are often benchmarked on simple physicochemical properties such as solubility or general druglikeness, which can be readily computed. However, these properties are poor representatives of objective functions in drug design, mainly because they do not depend on the candidate compound's interaction with the target. By contrast, molecular docking is a widely applied method in drug discovery to estimate binding affinities. However, docking studies require a significant amount of domain knowledge to set up correctly, which hampers adoption. Here, we present dockstring, a bundle for meaningful and robust comparison of ML models using docking scores. dockstring consists of three components: (1) an open-source Python package for straightforward computation of docking scores, (2) an extensive dataset of docking scores and poses of more than 260,000 molecules for 58 medically relevant targets, and (3) a set of pharmaceutically relevant benchmark tasks such as virtual screening or de novo design of selective kinase inhibitors. The Python package implements a robust ligand and target preparation protocol that allows nonexperts to obtain meaningful docking scores. Our dataset is the first to include docking poses, as well as the first of its size that is a full matrix, thus facilitating experiments in multiobjective optimization and transfer learning. Overall, our results indicate that docking scores are a more realistic evaluation objective than simple physicochemical properties, yielding benchmark tasks that are more challenging and more closely related to real problems in drug discovery.

In Vitro Cancer Models: A Closer Look at Limitations on Translation

In vitro cancer models are envisioned as high-throughput screening platforms for potential new therapeutic discovery and/or validation. They also serve as tools to achieve personalized treatment strategies or real-time monitoring of disease propagation, providing effective treatments to patients. To battle the fatality of metastatic cancers, the development and commercialization of predictive and robust preclinical in vitro cancer models are of urgent need. In the past decades, the translation of cancer research from 2D to 3D platforms and the development of diverse in vitro cancer models have been well elaborated in an enormous number of reviews. However, the meagre clinical success rate of cancer therapeutics urges the critical introspection of currently available preclinical platforms, including patents, to hasten the development of precision medicine and commercialization of in vitro cancer models. Hence, the present article critically reflects the difficulty of translating cancer therapeutics from discovery to adoption and commercialization in the light of in vitro cancer models as predictive tools. The state of the art of in vitro cancer models is discussed first, followed by identifying the limitations of bench-to-bedside transition. This review tries to establish compatibility between the current findings and obstacles and indicates future directions to accelerate the market penetration, considering the niche market.

Planar Chirality: A Mine for Catalysis and Structure Discovery

Planar chirality is one of the most fascinating expressions of chirality, which is exploited by nature to lock three-dimensional chiral conformations and, more recently, by chemists to create new chiral reagents, catalysts, and functional organic materials. Nevertheless, the shortage of procedures able to induce and secure asymmetry during the generation of these unique chiral entities has dissuaded chemists from exploiting their structural properties. This Minireview intends to illustrate the limited but remarkable catalytic methods that have been reported for the production of planar chirality in strained molecules and serve as a source of inspiration for the development of new unconventional procedures, which are expected to appear in the near future.

Engineering Hydrogels for the Development of Three-Dimensional In Vitro Models

The superiority of in vitro 3D cultures over conventional 2D cell cultures is well recognized by the scientific community for its relevance in mimicking the native tissue architecture and functionality. The recent paradigm shift in the field of tissue engineering toward the development of 3D in vitro models can be realized with its myriad of applications, including drug screening, developing alternative diagnostics, and regenerative medicine. Hydrogels are considered the most suitable biomaterial for developing an in vitro model owing to their similarity in features to the extracellular microenvironment of native tissue. In this review article, recent progress in the use of hydrogel-based biomaterial for the development of 3D in vitro biomimetic tissue models is highlighted. Discussions of hydrogel sources and the latest hybrid system with different combinations of biopolymers are also presented. The hydrogel crosslinking mechanism and design consideration are summarized, followed by different types of available hydrogel module systems along with recent microfabrication technologies. We also present the latest developments in engineering hydrogel-based 3D in vitro models targeting specific tissues. Finally, we discuss the challenges surrounding current in vitro platforms and 3D models in the light of future perspectives for an improved biomimetic in vitro organ system.

The Impact of Compensation Gap on Corporate Innovation: Evidence from China's Pharmaceutical Industry

The pharmaceutical industry is typically driven by innovation, and is relevant to people’s livelihoods. How to effectively motivate pharmaceutical enterprises to engage in innovative activities is a hot topic. On the basis of the perspective of the combined effect of tournament theory and social comparison theory, this study explored the impact of internal compensation gap on corporate innovation by using data from China’s listed pharmaceutical enterprises during 2011–2018. The findings show a nonlinear (inverted-U-shaped) relationship between compensation gap and corporate innovation within the pharmaceutical industry, which illustrates that the role of the compensation gap is not endless. We also find the optimal compensation gap between executives and employees. Further analyses indicate that this association is more pronounced in regions with low marketization levels, and in large enterprises. Moreover, the practical significance of the results is explored with an expectation of providing theoretical references for the pharmaceutical industry to establish reasonable incentive mechanisms and promote innovative development, and for the government to introduce innovation incentive policies.

An Overview of Nanotechnologies for Drug Delivery to the Brain

Drug delivery to the brain has been one of the toughest challenges researchers have faced to develop effective treatments for brain diseases. Owing to the blood–brain barrier (BBB), only a small portion of administered drug can reach the brain. A consequence of that is the need to administer a higher dose of the drug, which, expectedly, leads to a variety of unwanted side effects. Research in a variety of different fields has been underway for the past couple of decades to address this very serious and frequently lethal problem. One area of research that has produced optimistic results in recent years is nanomedicine. Nanomedicine is the science birthed by fusing the fields of nanotechnology, chemistry and medicine into one. Many different types of nanomedicine-based drug-delivery systems are currently being studied for the sole purpose of improved drug delivery to the brain. This review puts together and briefly summarizes some of the major breakthroughs in this crusade. Inorganic nanoparticle-based drug-delivery systems, such as gold nanoparticles and magnetic nanoparticles, are discussed, as well as some organic nanoparticulate systems. Amongst the organic drug-delivery nanosystems, polymeric micelles and dendrimers are discussed briefly and solid polymeric nanoparticles are explored in detail.