Neoclassic drug discovery: the case for lead generation using phenotypic and functional approaches

Phenotypic drug discovery: a case for thymosin alpha-1

Phenotypic drug discovery (PDD) involves screening compounds for their effects on cells, tissues, or whole organisms without necessarily understanding the underlying molecular targets. PDD differs from target-based strategies as it does not require knowledge of a specific drug target or its role in the disease. This approach can lead to the discovery of drugs with unexpected therapeutic effects or applications and allows for the identification of drugs based on their functional effects, rather than through a predefined target-based approach. Ultimately, disease definitions are mostly symptom-based rather than mechanism-based, and the therapeutics should be likewise. In recent years, there has been a renewed interest in PDD due to its potential to address the complexity of human diseases, including the holistic picture of multiple metabolites engaging with multiple targets constituting the central hub of the metabolic host-microbe interactions. Although PDD presents challenges such as hit validation and target deconvolution, significant achievements have been reached in the era of big data. This article explores the experiences of researchers testing the effect of a thymic peptide hormone, thymosin alpha-1, in preclinical and clinical settings and discuss how its therapeutic utility in the precision medicine era can be accommodated within the PDD framework.

A pan-respiratory antiviral chemotype targeting a transient host multi-protein complex

We present a novel small molecule antiviral chemotype that was identified by an unconventional cell-free protein synthesis and assembly-based phenotypic screen for modulation of viral capsid assembly. Activity of PAV-431, a representative compound from the series, has been validated against infectious viruses in multiple cell culture models for all six families of viruses causing most respiratory diseases in humans. In animals, this chemotype has been demonstrated efficacious for porcine epidemic diarrhoea virus (a coronavirus) and respiratory syncytial virus (a paramyxovirus). PAV-431 is shown to bind to the protein 14-3-3, a known allosteric modulator. However, it only appears to target the small subset of 14-3-3 which is present in a dynamic multi-protein complex whose components include proteins implicated in viral life cycles and in innate immunity. The composition of this target multi-protein complex appears to be modified upon viral infection and largely restored by PAV-431 treatment. An advanced analog, PAV-104, is shown to be selective for the virally modified target, thereby avoiding host toxicity. Our findings suggest a new paradigm for understanding, and drugging, the host-virus interface, which leads to a new clinical therapeutic strategy for treatment of respiratory viral disease.

Engineering cell-free systems by chemoproteomic-assisted phenotypic screening

Phenotypic screening is a valuable tool to both understand and engineer complex biological systems. We demonstrate the functionality of this approach in the development of cell-free protein synthesis (CFPS) technology. Phenotypic screening identified numerous compounds that enhanced protein production in yeast lysate CFPS reactions. Notably, many of these were competitive ATP kinase inhibitors, with the exploitation of their inherent substrate promiscuity redirecting ATP flux towards heterologous protein expression. Chemoproteomic-guided strain engineering partially phenocopied drug effects, with a 30% increase in protein yield observed upon deletion of the ATP-consuming SSA1 component of the HSP70 chaperone. Moreover, drug-mediated metabolic rewiring coupled with template optimization generated the highest protein yields in yeast CFPS to date using a hitherto less efficient, but more cost-effective glucose energy regeneration system. Our approach highlights the utility of target-agnostic phenotypic screening and target identification to deconvolute cell-lysate complexity, adding to the expanding repertoire of strategies for improving CFPS.

A Pan-Respiratory Antiviral Chemotype Targeting a Host Multi-Protein Complex

We present a novel small molecule antiviral chemotype that was identified by an unconventional cell-free protein synthesis and assembly-based phenotypic screen for modulation of viral capsid assembly. Activity of PAV-431, a representative compound from the series, has been validated against infectious virus in multiple cell culture models for all six families of viruses causing most respiratory disease in humans. In animals this chemotype has been demonstrated efficacious for Porcine Epidemic Diarrhea Virus (a coronavirus) and Respiratory Syncytial Virus (a paramyxovirus). PAV-431 is shown to bind to the protein 14-3-3, a known allosteric modulator. However, it only appears to target the small subset of 14-3-3 which is present in a dynamic multi-protein complex whose components include proteins implicated in viral lifecycles and in innate immunity. The composition of this target multi-protein complex appears to be modified upon viral infection and largely restored by PAV-431 treatment. Our findings suggest a new paradigm for understanding, and drugging, the host-virus interface, which leads to a new clinical therapeutic strategy for treatment of respiratory viral disease.

Engineering high throughput screening platforms of cervical cancer

Cervical cancer is the second leading cause of cancer-related death in women under 40 and is one of the few cancers to have an increased incidence rate and decreased survival rate over the last 10 years. One in five patients will have recurrent and/or distant metastatic disease and these patients face a 5-year survival rate of less than 17%. Thus, there is a pressing need to develop new anticancer therapeutics for this underserved patient population. However, the development of new anticancer drugs remains a challenge, as only 7% of novel anticancer drugs are approved for clinical use. To facilitate identification of novel and effective anticancer drugs for cervical cancer, we developed a multilayer multicellular platform of human cervical cancer cell lines and primary human microvascular endothelial cells that interfaces with high throughput drug screening methods to evaluate the anti-metastatic and anti-angiogenic drug efficacy simultaneously. Through the use of design of experiments statistical optimization, we identified the specific concentrations of collagen I, fibrinogen, fibronectin, GelMA, and PEGDA in each hydrogel layer that maximized both cervical cancer invasion and endothelial microvessel length. We then validated the optimized platform and assessed its viscoelastic properties. Finally, using this optimized platform, we conducted a targeted drug screen of four clinically relevant drugs on two cervical cancer cell lines. Overall, this work provides a valuable platform that can be used to screen large compound libraries for mechanistic studies, drug discovery, and precision oncology for cervical cancer patients.

Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries

The global spread of COVID-19 has raised the importance of pharmaceutical drug development as intractable and hot research. Developing new drug molecules to overcome any disease is a costly and lengthy process, but the process continues uninterrupted. The critical point to consider the drug design is to use the available data resources and to find new and novel leads. Once the drug target is identified, several interdisciplinary areas work together with artificial intelligence (AI) and machine learning (ML) methods to get enriched drugs. These AI and ML methods are applied in every step of the computer-aided drug design, and integrating these AI and ML methods results in a high success rate of hit compounds. In addition, this AI and ML integration with high-dimension data and its powerful capacity have taken a step forward. Clinical trials output prediction through the AI/ML integrated models could further decrease the clinical trials cost by also improving the success rate. Through this review, we discuss the backend of AI and ML methods in supporting the computer-aided drug design, along with its challenge and opportunity for the pharmaceutical industry. From the available information or data, the AI and ML based prediction for the high throughput virtual screening. After this integration of AI and ML, the success rate of hit identification has gained a momentum with huge success by providing novel drugs.

Screening and Purification of Natural Products from Actinomycetes that Induce a "Rounded" Morphological Phenotype in Fission Yeast

This study was designed to identify and investigate bioactive natural product compounds that alter the cellular shape of the fission yeast Schizosaccharomyces pombe and induce a "rounded" or "small" cellular morphological phenotype. Bioassays using a range of antifungal agents against a multidrug-sensitive fission yeast strain, SAK950 showed that many induced a "rounded" phenotype. We then investigated whether 46 of the actinomycete strains identified in our previous study as inducing a similar phenotype produced antifungal agents of similar classes. We show that five of the strains produced streptothricin and that 26 strains produced polyenes, including fungichromin, filipin and candicidin, the last of which was produced by 24 strains. A taxonomic study of the strains indicated that the majority of the candicidin only producers were Streptomyces hydrogenans and S. albidoflavus whilst those that additionally produced streptothricin were related to S. enissocaesilis. A follow-up study to investigate the natural products made by related strains indicated that they followed a similar pattern. The identification of several compounds from the actinomycete strains similar to the antifungal agents initially tested confirm the validity of an approach using the S. pombe morphological phenotype and actinomycete taxonomy as a predictive tool for natural product identification.

Isolation of a natural product with anti-mitotic activity from a toxic Canadian prairie plant

We are investigating plants from the prairie ecological zone of Canada to identify natural products that inhibit mitosis in cancer cells. Investigation of plant parts from the Canadian plant species Hymenoxys richardsonii (Asteraceae) revealed that leaf extracts (PP-360A) had anti-mitotic activity on human cancer cell lines. Cells treated with leaf extracts acquired a rounded morphology, similar to that of cells in mitosis. We demonstrated that the rounded cells contained mitotic spindles and phospho-histone H3 using the techniques of immunofluorescence microscopy. By biology-guided fractionation of H. richardsonii leaves, we isolated a sesquiterpene lactone named hymenoratin, which had not been previously assigned a biological activity. Cells treated with hymenoratin have phospho-histone H3 positive chromosomes, a mitotic spindle, and enter a prolonged mitotic arrest in which the spindles become distorted. By Western blot analysis, hymenoratin treated cells acquire high levels of cyclin B and dephosphorylated Cdk1. There is a growing body of evidence that select members of the sesquiterpene lactone chemical family have anti-mitotic activity.

Beyond Pseudo-natural Products: Sequential Ugi/Pictet-Spengler Reactions Leading to Steroidal Pyrazinoisoquinolines That Trigger Caspase-Independent Death in HepG2 Cells

In this work, we describe how stereochemically complex polycyclic compounds can be generated by applying a synthetic sequence comprising an intramolecular Ugi reaction followed by a Pictet-Spengler cyclization on steroid-derived scaffolds. The resulting compounds, which combine a fragment derived from a natural product and a scaffold not found in nature. are both structurally distinct and globally similar to natural products at the same time, and interrogate an alternative region of the chemical space. One of the new compounds showed significant antiproliferative activity on HepG2 cells through a caspase-independent cell-death mechanism, an appealing feature when new antitumor compounds are searched.

The right tools for the job: the central role for next generation chemical probes and chemistry-based target deconvolution methods in phenotypic drug discovery

The reconnection of the scientific community with phenotypic drug discovery has created exciting new possibilities to develop therapies for diseases with highly complex biology. It promises to revolutionise fields such as neurodegenerative disease and regenerative medicine, where the development of new drugs has consistently proved elusive. Arguably, the greatest challenge in readopting the phenotypic drug discovery approach exists in establishing a crucial chain of translatability between phenotype and benefit to patients in the clinic. This remains a key stumbling block for the field which needs to be overcome in order to fully realise the potential of phenotypic drug discovery. Excellent quality chemical probes and chemistry-based target deconvolution techniques will be a crucial part of this process. In this review, we discuss the current capabilities of chemical probes and chemistry-based target deconvolution methods and evaluate the next advances necessary in order to fully support phenotypic screening approaches in drug discovery.

[From high content screening to target deconvolution: New insights for phenotypic approaches]

The advent of the molecular biology and the completion of the human genome sequencing prompted the pharmaceutical industry to progressively implement target-centric drug discovery strategies. However, concerns regarding the research and development productivity during the last ten years, combined with technological developments in high-content screening, automation, image analysis and artificial intelligence triggered a renewed interest for the phenotypic drug discovery approaches. Target-centric and phenotypic approaches are more and more considered complementary, hence, positioning the target deconvolution on the critical path. This review analyzes the evolution of the target-centric and phenotypic approaches, focusing more specifically on the high-content screening and the target deconvolution technologies currently available.

Use of artificial intelligence to enhance phenotypic drug discovery

Research and development (R&D) productivity across the pharmaceutical industry has received close scrutiny over the past two decades, especially taking into consideration reports of attrition rates and the colossal cost for drug development. The respective merits of the two main drug discovery approaches, phenotypic and target based, have divided opinion across the research community, because each hold different advantages for identifying novel molecular entities with a successful path to the market. Nevertheless, both have low translatability in the clinic. Artificial intelligence (AI) and adoption of machine learning (ML) tools offer the promise of revolutionising drug development, and overcoming obstacles in the drug discovery pipeline. Here, we assess the potential of target-driven and phenotypic-based approaches and offer a holistic description of the current state of the field, from both a scientific and industry perspective. With the emerging partnerships between AI/ML and pharma still in their relative infancy, we investigate the potential and current limitations with a particular focus on phenotypic drug discovery. Finally, we emphasise the value of public-private partnerships (PPPs) and cross-disciplinary collaborations to foster innovation and facilitate efficient drug discovery programmes.

Improving target assessment in biomedical research: the GOT-IT recommendations

Academic research plays a key role in identifying new drug targets, including understanding target biology and links between targets and disease states. To lead to new drugs, however, research must progress from purely academic exploration to the initiation of efforts to identify and test a drug candidate in clinical trials, which are typically conducted by the biopharma industry. This transition can be facilitated by a timely focus on target assessment aspects such as target-related safety issues, druggability and assayability, as well as the potential for target modulation to achieve differentiation from established therapies. Here, we present recommendations from the GOT-IT working group, which have been designed to support academic scientists and funders of translational research in identifying and prioritizing target assessment activities and in defining a critical path to reach scientific goals as well as goals related to licensing, partnering with industry or initiating clinical development programmes. Based on sets of guiding questions for different areas of target assessment, the GOT-IT framework is intended to stimulate academic scientists' awareness of factors that make translational research more robust and efficient, and to facilitate academia-industry collaboration.

P450s under Restriction (PURE) Screen Using HepaRG and Primary Human Hepatocytes for Discovery of Novel HBV Antivirals

Herein is reported a novel screening paradigm PURE (P450s under restriction) for the identification and optimization of hits as part of a hepatitis B virus (HBV) antiviral discovery program. To closely represent in vivo hepatocytes, differentiated HepaRG cells (dHRGs) and primary human hepatocytes (PHHs) were used as the basis for an HBV infection system. However, a significant challenge arose during potency evaluation in using cultured dHRGs and PHHs as screening platforms because, as with hepatocytes in vivo, these cells express active cytochrome P450 enzymes and thus can metabolize test compounds. The observed antiviral effects may be the cumulative result of a dynamic pool of parent compound and metabolites thus confounding structure activity relationship (SAR) interpretation and subsequent optimization design initiatives. We show here that PURE methodology restricts metabolism of HBV-infected dHRGs and PHHs and thus provides highly informative potency data for decision-making on key representative antiviral compounds.

Recent advances in phenotypic drug discovery

There is a great need for innovative new medicines to treat unmet medical needs. The discovery and development of innovative new medicines is extremely difficult, costly, and inefficient. In the last decade, phenotypic drug discovery (PDD) was reintroduced as a strategy to provide first-in-class medicines. PDD uses empirical, target-agnostic lead generation to identify pharmacologically active molecules and novel therapeutics which work through unprecedented drug mechanisms. The economic and scientific value of PDD is exemplified through game-changing medicines for hepatitis C virus, spinal muscular atrophy, and cystic fibrosis. In this short review, recent advances are noted for the implementation and de-risking of PDD (for compound library selection, biomarker development, mechanism identification, and safety studies) and the potential for artificial intelligence. A significant barrier in the decision to implement PDD is balancing the potential impact of a novel mechanism of drug action with an under-defined scientific path forward, with the desire to provide infrastructure and metrics to optimize return on investment, which a known mechanism provides. A means to address this knowledge gap in the future is to empower precompetitive research utilizing the empirical concepts of PDD to identify new mechanisms and pharmacologically active compounds.

A Phenotarget Approach for Identifying an Alkaloid Interacting with the Tuberculosis Protein Rv1466

In recent years, there has been a revival of interest in phenotypic-based drug discovery (PDD) due to target-based drug discovery (TDD) falling below expectations. Both PDD and TDD have their unique advantages and should be used as complementary methods in drug discovery. The PhenoTarget approach combines the strengths of the PDD and TDD approaches. Phenotypic screening is conducted initially to detect cellular active components and the hits are then screened against a panel of putative targets. This PhenoTarget protocol can be equally applied to pure compound libraries as well as natural product fractions. Here we described the use of the PhenoTarget approach to identify an anti-tuberculosis lead compound. Fractions from Polycarpa aurata were identified with activity against Mycobacterium tuberculosis H37Rv. Native magnetic resonance mass spectrometry (MRMS) against a panel of 37 proteins from Mycobacterium proteomes showed that a fraction from a 95% ethanol re-extraction specifically formed a protein-ligand complex with Rv1466, a putative uncharacterized Mycobacterium tuberculosis protein. The natural product responsible was isolated and characterized to be polycarpine. The molecular weight of the ligand bound to Rv1466, 233 Da, was half the molecular weight of polycarpine less one proton, indicating that polycarpine formed a covalent bond with Rv1466.

Powered by Open Innovation: Opportunities and Challenges in the Pharma Sector

The value of innovation in medicines is clear. Despite all of the progress in the twenty-first century, there are still many unmet medical needs and opportunities to improve healthcare. The challenges for pharmaceutical companies include ways in which to stay competitive and flexible in an environment of constant knowledge growth and increasingly sophisticated technologies, and ways to generate sufficient revenues to sustain their own growth. To that end, pharmaceutical companies are compelled to adapt different business models in the face of new challenges. The industry is plagued with long research and development (R&D) cycles and low success rates for innovative treatments; something has to change. The need to collaborate externally across the process of discovery, development, manufacturing and commercialization is a must. Furthermore, collaborations have increased in frequency and scope, expanding the opportunities to access global scientific talent in academia, research institutes and biotechnology companies. Despite the perception that pharma companies are 'closed' or tightly controlled industries, open innovation is already well established in the pharmaceutical sector and used to supplement R&D in the process of bringing new medicines for patients faster, and at a lower cost. Over the years, each pharma company has tailored the open-innovation concept to develop its own model based on particular needs and offerings. Independently of the model, the creation of successful partnerships in external innovation requires reaching out and connecting beyond the traditional organizational boundaries. Substantial internal cultural changes are required to implement open-innovation strategies that should co-exist without competing with the traditional ways of operating. Major changes bring challenges but create multiple opportunities for scientists and organizations. High-quality drug discovery requires continuous learning and an open way of thinking to adopt novel operational models and to implement efficient collaborations.

The Convergence of Stem Cell Technologies and Phenotypic Drug Discovery

Recent advances in induced pluripotent stem cell technologies and phenotypic screening shape the future of bioactive small-molecule discovery. In this review we analyze the impact of small-molecule phenotypic screens on drug discovery as well as on the investigation of human development and disease biology. We further examine the role of 3D spheroid/organoid structures, microfluidic systems, and miniaturized on-a-chip systems for future discovery strategies. In highlighting representative examples, we analyze how recent achievements can translate into future therapies. Finally, we discuss remaining challenges that need to be overcome for the adaptation of the next generation of screening approaches.

Towards High-Throughput Chemobehavioural Phenomics in Neuropsychiatric Drug Discovery

Identifying novel marine-derived neuroactive chemicals with therapeutic potential is difficult due to inherent complexities of the central nervous system (CNS), our limited understanding of the molecular foundations of neuro-psychiatric conditions, as well as the limited applications of effective high-throughput screening models that recapitulate functionalities of the intact CNS. Furthermore, nearly all neuro-modulating chemicals exhibit poorly characterized pleiotropic activities often referred to as polypharmacology. The latter renders conventional target-based in vitro screening approaches very difficult to accomplish. In this context, chemobehavioural phenotyping using innovative small organism models such as planarians and zebrafish represent powerful and highly integrative approaches to study the impact of new chemicals on central and peripheral nervous systems. In contrast to in vitro bioassays aimed predominantly at identification of chemicals acting on single targets, phenotypic chemobehavioural analysis allows for complex multi-target interactions to occur in combination with studies of polypharmacological effects of chemicals in a context of functional and intact milieu of the whole organism. In this review, we will outline recent advances in high-throughput chemobehavioural phenotyping and provide a future outlook on how those innovative methods can be utilized for rapidly screening and characterizing marine-derived compounds with prospective applications in neuropharmacology and psychosomatic medicine.

Antibiotic Discovery: Where Have We Come from, Where Do We Go?

Given the increase in antibiotic-resistant bacteria, alongside the alarmingly low rate of newly approved antibiotics for clinical usage, we are on the verge of not having effective treatments for many common infectious diseases. Historically, antibiotic discovery has been crucial in outpacing resistance and success is closely related to systematic procedures—platforms—that have catalyzed the antibiotic golden age, namely the Waksman platform, followed by the platforms of semi-synthesis and fully synthetic antibiotics. Said platforms resulted in the major antibiotic classes: aminoglycosides, amphenicols, ansamycins, beta-lactams, lipopeptides, diaminopyrimidines, fosfomycins, imidazoles, macrolides, oxazolidinones, streptogramins, polymyxins, sulphonamides, glycopeptides, quinolones and tetracyclines. During the genomics era came the target-based platform, mostly considered a failure due to limitations in translating drugs to the clinic. Therefore, cell-based platforms were re-instituted, and are still of the utmost importance in the fight against infectious diseases. Although the antibiotic pipeline is still lackluster, especially of new classes and novel mechanisms of action, in the post-genomic era, there is an increasingly large set of information available on microbial metabolism. The translation of such knowledge into novel platforms will hopefully result in the discovery of new and better therapeutics, which can sway the war on infectious diseases back in our favor.

MolTarPred: A web tool for comprehensive target prediction with reliability estimation

Molecular target prediction can provide a starting point to understand the efficacy and side effects of phenotypic screening hits. Unfortunately, the vast majority of in silico target prediction methods are not available as web tools. Furthermore, these are limited in the number of targets that can be predicted, do not estimate which target predictions are more reliable and/or lack comprehensive retrospective validations. We present MolTarPred ( http://moltarpred.marseille.inserm.fr/), a user-friendly web tool for predicting protein targets of small organic compounds. It is powered by a large knowledge base comprising 607,659 compounds and 4,553 macromolecular targets collected from the ChEMBL database. In about 1 min, the predicted targets for the supplied molecule will be listed in a table. The chemical structures of the query molecule and the most similar compounds annotated with the predicted target will also be shown to permit visual inspection and comparison. Practical examples of the use of MolTarPred are showcased. MolTarPred is a new resource for scientists that require a more complete knowledge of the polypharmacology of a molecule. The introduction of a reliability score constitutes an attractive functionality of MolTarPred, as it permits focusing experimental confirmatory tests on the most reliable predictions, which leads to higher prospective hit rates.

Doppler fluctuation spectroscopy of intracellular dynamics in living tissue

Intracellular dynamics in living tissue are dominated by active transport driven by bioenergetic processes far from thermal equilibrium. Intracellular constituents typically execute persistent walks. In the limit of long mean free paths, the persistent walks are ballistic, exhibiting a "Doppler edge" in light scattering fluctuation spectra. At shorter transport lengths, the fluctuations are described by lifetime-broadened Doppler spectra. Dynamic light scattering from transport in the ballistic, diffusive, or the crossover regimes is derived analytically, including the derivation of autocorrelation functions through a driven damped harmonic oscillator analog for light scattering from persistent walks. The theory is validated through Monte Carlo simulations. Experimental evidence for the Doppler edge in three-dimensional (3D) living tissue is obtained using biodynamic imaging based on low-coherence interferometry and digital holography.

A11, a novel diaryl acylhydrazone derivative, exerts neuroprotection against ischemic injury in vitro and in vivo

There is an urgent need to develop effective therapies for ischemic stroke, but the complicated pathological processes after ischemia make doing so difficult. In the current study, we identified a novel diaryl acylhydrazone derivative, A11, which has multiple neuroprotective properties in ischemic stroke models. First, A11 was demonstrated to induce neuroprotection against ischemic injury in a dose-dependent manner (from 0.3 to 3 μM) in three in vitro experimental ischemic stroke models: oxygen glucose deprivation (OGD), hydrogen peroxide, and glutamate-stimulated neuronal cell injury models. Moreover, A11 was able to potently alleviate three critical pathological changes, apoptosis, oxidative stress, and mitochondrial dysfunction, following ischemic insult in neuronal cells. Further analysis revealed that A11 upregulated the phosphorylation levels of protein kinase B (AKT) and extracellular signal-regulated kinase (ERK) in OGD-exposed neuronal cells, suggesting joint activation of the phosphoinositide 3-kinase (PI3K)/AKT and mitogen-activated protein kinase (MEK)/ERK pathways. In rats with middle cerebral artery occlusion, single-dose administration of A11 (3 mg/kg per day, i.v.) at the onset of reperfusion significantly reduced the infarct volumes and ameliorated neurological deficits. Our study, for the first time, reports the anti-ischemic effect of diaryl acylhydrazone chemical entities, especially A11, which acts on multiple ischemia-associated pathological processes. Our results may provide new clues for the development of an effective therapeutic agent for ischemic stroke.

A Novel Multiple-Read Screen for Metabolically Active Compounds Based on a Genetically Encoded FRET Sensor for ATP

Both glycolysis and mitochondrial energetics are targets of interest for developing antiproliferative cancer therapeutics. We developed a novel multiple-read assay based on long-term expression in K562 cells of a genetically encoded intramolecular Förster resonance energy transfer sensor for adenosine triphosphate (ATP). The assay, conducted in a fluorescent plate reader, can identify compounds that inhibit oxidative phosphorylation-dependent ATP production, glycolysis, or both after short-term treatment. We screened a National Cancer Institute (NCI) compound library, identifying inhibitors of oxidative phosphorylation-dependent ATP production and glycolysis. Three glycolysis inhibitors blocked hexokinase activity, demonstrating that our assay can serve as the initial step in a workflow to identify compounds that inhibit glycolysis via a defined desired mechanism. Finally, upon reviewing the literature, we found surprisingly little evidence that inhibiting glycolysis with small molecules is antiproliferative. Using NCI data on proliferation of K562 cells, we found that inhibitors of oxidative phosphorylation-dependent ATP production were no more antiproliferative than the overall library, whereas all glycolysis inhibitors were in the top third of most effective antiproliferative compounds. Our results thus present a powerful new way to screen for compounds that affect cellular metabolism and also provide important support for the idea that blocking glycosis is antiproliferative.

Biodynamic digital holography of chemoresistance in a pre-clinical trial of canine B-cell lymphoma

Biodynamic digital holography was used to obtain phenotypic profiles of canine non-Hodgkin B-cell lymphoma biopsies treated with standard-of-care chemotherapy. Biodynamic signatures from the living 3D tissues were extracted using fluctuation spectroscopy from intracellular Doppler light scattering in response to the molecular mechanisms of action of therapeutic drugs that modify a range of internal cellular motions. The standard-of-care to treat B-cell lymphoma in both humans and dogs is a combination CHOP therapy that consists of doxorubicin, prednisolone, cyclophosphamide and vincristine. The proportion of dogs experiencing durable cancer remission following CHOP chemotherapy was 68%, with 13 out of 19 dogs responding favorably to therapy and 6 dogs failing to have progression-free survival times greater than 100 days. Biodynamic signatures were found that correlate with inferior survival times, and biomarker selection was optimized to identify specific Doppler signatures related to chemoresistance. A machine learning classifier was constructed based on feature vector correlations and linear separability in high-dimensional feature space. Hold-out validation predicted patient response to therapy with 84% accuracy. These results point to the potential for biodynamic profiling to contribute to personalized medicine by aiding the selection of chemotherapy for cancer patients.

The Current Landscape of 3D In Vitro Tumor Models: What Cancer Hallmarks Are Accessible for Drug Discovery?

Cancer prognosis remains a lottery dependent on cancer type, disease stage at diagnosis, and personal genetics. While investment in research is at an all-time high, new drugs are more likely to fail in clinical trials today than in the 1970s. In this review, a summary of current survival statistics in North America is provided, followed by an overview of the modern drug discovery process, classes of models used throughout different stages, and challenges associated with drug development efficiency are highlighted. Then, an overview of the cancer hallmarks that drive clinical progression is provided, and the range of available clinical therapies within the context of these hallmarks is categorized. Specifically, it is found that historically, the development of therapies is limited to a subset of possible targets. This provides evidence for the opportunities offered by novel disease-relevant in vitro models that enable identification of novel targets that facilitate interactions between the tumor cells and their surrounding microenvironment. Next, an overview of the models currently reported in literature is provided, and the cancer biology they have been used to explore is highlighted. Finally, four priority areas are suggested for the field to accelerate adoption of in vitro tumour models for cancer drug discovery.

Application of a phenotypic drug discovery strategy to identify biological and chemical starting points for inhibition of TSLP production in lung epithelial cells

Thymic stromal lymphopoietin (TSLP) is a cytokine released by human lung epithelium in response to external insult. Considered as a master switch in T helper 2 lymphocyte (Th2) mediated responses, TSLP is believed to play a key role in allergic diseases including asthma. The aim of this study was to use a phenotypic approach to identify new biological and chemical starting points for inhibition of TSLP production in human bronchial epithelial cells (NHBE), with the objective of reducing Th2-mediated airway inflammation. To this end, a phenotypic screen was performed using poly I:C / IL-4 stimulated NHBE cells interrogated with a 44,974 compound library. As a result, 85 hits which downregulated TSLP protein and mRNA levels were identified and a representative subset of 7 hits was selected for further characterization. These molecules inhibited the activity of several members of the MAPK, PI3K and tyrosine kinase families and some of them have been reported as modulators of cellular phenotypic endpoints like cell-cell contacts, microtubule polymerization and caspase activation. Characterization of the biological profile of the hits suggested that mTOR could be a key activity involved in the regulation of TSLP production in NHBE cells. Among other targeted kinases, inhibition of p38 MAPK and JAK kinases showed different degrees of correlation with TSLP downregulation, while Syk kinase did not seem to be related. Overall, inhibition of TSLP production by the selected hits, rather than resulting from inhibition of single isolated targets, appeared to be due to a combination of activities with different levels of relevance. Finally, a hit expansion exercise yielded additional active compounds that could be amenable to further optimization, providing an opportunity to dissociate TSLP inhibition from other non-desired activities. This study illustrates the potential of phenotypic drug discovery to complement target based approaches by providing new chemistry and biology leads.

Protein complexes as psychiatric and neurological drug targets

The need for improved medications for psychiatric and neurological disorders is clear. Difficulties in finding such drugs demands that all strategic means be utilized for their invention. The discovery of forebrain specific AMPA receptor antagonists, which selectively block the specific combinations of principal and auxiliary subunits present in forebrain regions but spare targets in the cerebellum, was recently disclosed. This discovery raised the possibility that other auxiliary protein systems could be utilized to help identify new medicines. Discussion of the TARP-dependent AMPA receptor antagonists has been presented elsewhere. Here we review the diversity of protein complexes of neurotransmitter receptors in the nervous system to highlight the broad range of protein/protein drug targets. We briefly outline the structural basis of protein complexes as drug targets for G-protein-coupled receptors, voltage-gated ion channels, and ligand-gated ion channels. This review highlights heterodimers, subunit-specific receptor constructions, multiple signaling pathways, and auxiliary proteins with an emphasis on the later. We conclude that the use of auxiliary proteins in chemical compound screening could enhance the detection of specific, targeted drug searches and lead to novel and improved medicines for psychiatric and neurological disorders.

Translating Knowledge Into Therapy for Acute Kidney Injury

No therapies have been shown to improve outcomes in patients with acute kidney injury (AKI). Given the high morbidity and mortality associated with AKI this represents an important unmet medical need. A common feature of all of the therapeutic development efforts for AKI is that none were driven by target selection or preclinical modeling that was based primarily on human data. This is important when considering a heterogeneous and dynamic condition such as AKI, in which in the absence of more accurate molecular classifications, clinical cohorts are likely to include patients with different types of injury at different stages in the injury and repair continuum. The National Institutes of Health precision medicine initiative offers an opportunity to address this. By creating a molecular tissue atlas of AKI, defining patient subgroups, and identifying critical cells and pathways involved in human AKI, this initiative has the potential to transform our current approach to therapeutic discovery. In this review, we discuss the opportunities and challenges that this initiative presents, with a specific focus on AKI, what additional efforts will be needed to apply these discoveries to therapeutic development, and how we believe this effort might lead to the development of new therapeutics for subsets of patients with AKI.

Drug Discovery in Fish, Flies, and Worms

Nonmammalian model organisms such as the nematode Caenorhabditis elegans, the fruit fly Drosophila melanogaster, and the zebrafish Danio rerio provide numerous experimental advantages for drug discovery including genetic and molecular tractability, amenability to high-throughput screening methods and reduced experimental costs and increased experimental throughput compared to traditional mammalian models. An interdisciplinary approach that strategically combines the study of nonmammalian and mammalian animal models with diverse experimental tools has and will continue to provide deep molecular and genetic understanding of human disease and will significantly enhance the discovery and application of new therapies to treat those diseases. This review will provide an overview of C. elegans, Drosophila, and zebrafish biology and husbandry and will discuss how these models are being used for phenotype-based drug screening and for identification of drug targets and mechanisms of action. The review will also describe how these and other nonmammalian model organisms are uniquely suited for the discovery of drug-based regenerative medicine therapies.

The Oncopig Cancer Model as a Complementary Tool for Phenotypic Drug Discovery

The screening of potential therapeutic compounds using phenotypic drug discovery (PDD) is being embraced once again by researchers and pharmaceutical companies as an approach to enhance the development of new effective therapeutics. Before the genomics and molecular biology era and the consecutive emergence of targeted-drug discovery approaches, PDD was the most common platform used for drug discovery. PDD, also known as phenotypic screening, consists of screening potential compounds in either in vitro cellular or in vivo animal models to identify compounds resulting in a desirable phenotypic change. Using this approach, the biological targets of the compounds are not taken into consideration. Suitable animal models are crucial for the continued validation and discovery of new drugs, as compounds displaying promising results in phenotypic in vitro cell-based and in vivo small animal model screenings often fail in clinical trials. Indeed, this is mainly a result of differential anatomy, physiology, metabolism, immunology, and genetics between humans and currently used pre-clinical small animal models. In contrast, pigs are more predictive of therapeutic treatment outcomes in humans than rodents. In addition, pigs provide an ideal platform to study cancer due to their similarities with humans at the anatomical, physiological, metabolic, and genetic levels. Here we provide a mini-review on the reemergence of PDD in drug development, highlighting the potential of porcine cancer models for improving pre-clinical drug discovery and testing. We also present precision medicine based genetically defined swine cancer models developed to date and their potential as biomedical models.

Phenotypic Screen Identifies a Small Molecule Modulating ERK2 and Promoting Stem Cell Proliferation

Stem cells display a fundamentally different mechanism of proliferation control when compared to somatic cells. Uncovering these mechanisms would maximize the impact in drug discovery with a higher translational applicability. The unbiased approach used in phenotype-based drug discovery (PDD) programs can offer a unique opportunity to identify such novel biological phenomenon. Here, we describe an integrated phenotypic screening approach, employing a combination of in vitro and in vivo PDD models to identify a small molecule increasing stem cell proliferation. We demonstrate that a combination of both in vitro and in vivo screening models improves hit identification and reproducibility of effects across various PDD models. Using cell viability and colony size phenotype measurement we characterize the structure activity relationship of the lead molecule, and identify that the small molecule inhibits phosphorylation of ERK2 and promotes stem cell proliferation. This study demonstrates a PDD approach that employs combinatorial models to identify compounds promoting stem cell proliferation.

Development of the Theta Comparative Cell Scoring Method to Quantify Diverse Phenotypic Responses Between Distinct Cell Types

In this article, we have developed novel data visualization tools and a Theta comparative cell scoring (TCCS) method, which supports high-throughput in vitro pharmacogenomic studies across diverse cellular phenotypes measured by multiparametric high-content analysis. The TCCS method provides a univariate descriptor of divergent compound-induced phenotypic responses between distinct cell types, which can be used for correlation with genetic, epigenetic, and proteomic datasets to support the identification of biomarkers and further elucidate drug mechanism-of-action. Application of these methods to compound profiling across high-content assays incorporating well-characterized cells representing known molecular subtypes of disease supports the development of personalized healthcare strategies without prior knowledge of a drug target. We present proof-of-principle data quantifying distinct phenotypic response between eight breast cancer cells representing four disease subclasses. Application of the TCCS method together with new advances in next-generation sequencing, induced pluripotent stem cell technology, gene editing, and high-content phenotypic screening are well placed to advance the identification of predictive biomarkers and personalized medicine approaches across a broader range of disease types and therapeutic classes.

Opportunities and challenges in phenotypic drug discovery: an industry perspective

Phenotypic drug discovery (PDD) approaches do not rely on knowledge of the identity of a specific drug target or a hypothesis about its role in disease, in contrast to the target-based strategies that have been widely used in the pharmaceutical industry in the past three decades. However, in recent years, there has been a resurgence in interest in PDD approaches based on their potential to address the incompletely understood complexity of diseases and their promise of delivering first-in-class drugs, as well as major advances in the tools for cell-based phenotypic screening. Nevertheless, PDD approaches also have considerable challenges, such as hit validation and target deconvolution. This article focuses on the lessons learned by researchers engaged in PDD in the pharmaceutical industry and considers the impact of 'omics' knowledge in defining a cellular disease phenotype in the era of precision medicine, introducing the concept of a chain of translatability. We particularly aim to identify features and areas in which PDD can best deliver value to drug discovery portfolios and can contribute to the identification and the development of novel medicines, and to illustrate the challenges and uncertainties that are associated with PDD in order to help set realistic expectations with regard to its benefits and costs.

Fragment-based drug discovery as alternative strategy to the drug development for neglected diseases

Neglected diseases (NDs) affect large populations and almost whole continents, representing 12% of the global health burden. In contrast, the treatment available today is limited and sometimes ineffective. Under this scenery, the Fragment-Based Drug Discovery emerged as one of the most promising alternatives to the traditional methods of drug development. This method allows achieving new lead compounds with smaller size of fragment libraries. Even with the wide Fragment-Based Drug Discovery success resulting in new effective therapeutic agents against different diseases, until this moment few studies have been applied this approach for NDs area. In this article, we discuss the basic Fragment-Based Drug Discovery process, brief successful ideas of general applications and show a landscape of its use in NDs, encouraging the implementation of this strategy as an interesting way to optimize the development of new drugs to NDs.

Predicting the Reliability of Drug-target Interaction Predictions with Maximum Coverage of Target Space

Many computational methods to predict the macromolecular targets of small organic molecules have been presented to date. Despite progress, target prediction methods still have important limitations. For example, the most accurate methods implicitly restrict their predictions to a relatively small number of targets, are not systematically validated on drugs (whose targets are harder to predict than those of non-drug molecules) and often lack a reliability score associated with each predicted target. Here we present a systematic validation of ligand-centric target prediction methods on a set of clinical drugs. These methods exploit a knowledge-base covering 887,435 known ligand-target associations between 504,755 molecules and 4,167 targets. Based on this dataset, we provide a new estimate of the polypharmacology of drugs, which on average have 11.5 targets below IC50 10 µM. The average performance achieved across clinical drugs is remarkable (0.348 precision and 0.423 recall, with large drug-dependent variability), especially given the unusually large coverage of the target space. Furthermore, we show how a sparse ligand-target bioactivity matrix to retrospectively validate target prediction methods could underestimate prospective performance. Lastly, we present and validate a first-in-kind score capable of accurately predicting the reliability of target predictions.

Drug Discovery to Halt the Progression of Acute Kidney Injury to Chronic Kidney Disease: A Case for Phenotypic Drug Discovery in Acute Kidney Injury

The cellular responses that occur following acute kidney injury (AKI) are complex and dynamic, involving multiple cells types and molecular pathways. For this reason, early selection of defined molecular targets for therapeutic intervention is unlikely to be effective in complex in vivo models of AKI, let alone Phase 3 clinical trials in patients with even more complex AKI pathobiology. Phenotypic screening using zebrafish provides an attractive alternative that does not require prior knowledge of molecular targets and may identify compounds that modify multiple targets that might be missed in more traditional target-based screens. In this review, we discuss results of an academic drug discovery campaign that used zebrafish as a primary screening tool to discover compounds with favorable absorption, metabolism, and toxicity that enhance repair when given late after injury in multiple models of AKI. We discuss how this screening campaign is being integrated into a more comprehensive, phenotypic, and target-based screen for lead compound optimization.

Phenylpyrrolidine structural mimics of pirfenidone lacking antifibrotic activity: A new tool for mechanism of action studies

Pirfenidone recently received FDA approval as one of the first two drugs designed to treat idiopathic pulmonary fibrosis. While the clinical data continues to support the efficacy of pirfenidone, the specific molecular mechanism of action of this drug has not been fully defined. From a chemical perspective the comparatively simple and lipophilic structure of pirfenidone combined with its administration at high doses, both experimentally and clinically, complicates some of the basic tenants of drug action and drug design. Our objective here was to identify a commercially available structural mimic of pirfenidone which retains key aspects of its physical chemical properties but does not display any of its antifibrotic effects. We tested these molecules using lung fibroblasts derived from patients with idiopathic pulmonary fibrosis and found phenylpyrrolidine based analogs of pirfenidone that were non-toxic and lacked antifibrotic activity even when applied at millimolar concentrations. Based on our findings, these molecules represent pharmacological tools for future studies delineating pirfenidone's mechanism of action.

In Vivo Systems Response Profiling and Multivariate Classification of CNS Active Compounds: A Structured Tool for CNS Drug Discovery

This paper describes the application of in vivo systems response profiling in CNS drug discovery by a process referred to as the Integrative Screening Process. The biological response profile, treated as an array, is used as major outcome for selection of candidate drugs. Dose-response data, including ex vivo brain monoaminergic biomarkers and behavioral descriptors, are systematically collected and analyzed by principal component analysis (PCA) and partial least-squares (PLS) regression, yielding multivariate characterization across compounds. The approach is exemplified by assessing a new class of CNS active compounds, the dopidines, compared to other monoamine modulating compounds including antipsychotics, antidepressants, and procognitive agents. Dopidines display a distinct phenotypic profile which has prompted extensive further preclinical and clinical investigations. In summary, in vivo profiles of CNS compounds are mapped, based on dose response studies in the rat. Applying a systematic and standardized work-flow, a database of in vivo systems response profiles is compiled, enabling comparisons and classification. This creates a framework for translational mapping, a crucial component in CNS drug discovery.

Accelerating glioblastoma drug discovery: Convergence of patient-derived models, genome editing and phenotypic screening

Patients diagnosed with glioblastoma (GBM) continue to face a bleak prognosis. It is critical that new effective therapeutic strategies are developed. GBM stem cells have molecular hallmarks of neural stem and progenitor cells and it is possible to propagate both non-transformed normal neural stem cells and GBM stem cells, in defined, feeder-free, adherent culture. These primary stem cell lines provide an experimental model that is ideally suited to cell-based drug discovery or genetic screens in order to identify tumour-specific vulnerabilities. For many solid tumours, including GBM, the genetic disruptions that drive tumour initiation and growth have now been catalogued. CRISPR/Cas-based genome editing technologies have recently emerged, transforming our ability to functionally annotate the human genome. Genome editing opens prospects for engineering precise genetic changes in normal and GBM-derived neural stem cells, which will provide more defined and reliable genetic models, with critical matched pairs of isogenic cell lines. Generation of more complex alleles such as knock in tags or fluorescent reporters is also now possible. These new cellular models can be deployed in cell-based phenotypic drug discovery (PDD). Here we discuss the convergence of these advanced technologies (iPS cells, neural stem cell culture, genome editing and high content phenotypic screening) and how they herald a new era in human cellular genetics that should have a major impact in accelerating glioblastoma drug discovery.

Applications of chemogenomic library screening in drug discovery

The allure of phenotypic screening, combined with the industry preference for target-based approaches, has prompted the development of innovative chemical biology technologies that facilitate the identification of new therapeutic targets for accelerated drug discovery. A chemogenomic library is a collection of selective small-molecule pharmacological agents, and a hit from such a set in a phenotypic screen suggests that the annotated target or targets of that pharmacological agent may be involved in perturbing the observable phenotype. In this Review, we describe opportunities for chemogenomic screening to considerably expedite the conversion of phenotypic screening projects into target-based drug discovery approaches. Other applications are explored, including drug repositioning, predictive toxicology and the discovery of novel pharmacological modalities.

Biodynamic imaging for phenotypic profiling of three-dimensional tissue culture

Three-dimensional (3-D) tissue culture represents a more biologically relevant environment for testing new drugs compared to conventional two-dimensional cancer cell culture models. Biodynamic imaging is a high-content 3-D optical imaging technology based on low-coherence interferometry and digital holography that uses dynamic speckle as high-content image contrast to probe deep inside 3-D tissue. Speckle contrast is shown to be a scaling function of the acquisition time relative to the persistence time of intracellular transport and hence provides a measure of cellular activity. Cellular responses of 3-D multicellular spheroids to paclitaxel are compared among three different growth techniques: rotating bioreactor (BR), hanging-drop (HD), and nonadherent (U-bottom, UB) plate spheroids, compared with ex vivo living tissues. HD spheroids have the most homogeneous tissue, whereas BR spheroids display large sample-to-sample variability as well as spatial heterogeneity. The responses of BR-grown tumor spheroids to paclitaxel are more similar to those of ex vivo biopsies than the responses of spheroids grown using HD or plate methods. The rate of mitosis inhibition by application of taxol is measured through tissue dynamics spectroscopic imaging, demonstrating the ability to monitor antimitotic chemotherapy. These results illustrate the potential use of low-coherence digital holography for 3-D pharmaceutical screening applications.