Drug Discovery in Academia- the third way?

Exploring molecular fragments for fraction unbound in human plasma of chemicals: a fragment-based cheminformatics approach

Fraction unbound in plasma (fu,p) of drugs is an significant factor for drug delivery and other biological incidences related to the pharmacokinetic behaviours of drugs. Exploration of different molecular fragments for fu,p of different small molecules/agents can facilitate in identification of suitable candidates in the preliminary stage of drug discovery. Different researchers have implemented strategies to build several prediction models for fu,p of different drugs. However, these studies did not focus on the identification of responsible molecular fragments to determine the fraction unbound in plasma. In the current work, we tried to focus on the development of robust classification-based QSAR models and evaluated these models with multiple statistical metrics to identify essential molecular fragments/structural attributes for fractions unbound in plasma. The study unequivocally suggests various N-containing aromatic rings and aliphatic groups have positive influences and sulphur-containing thiadiazole rings have negative influences for the fu,p values. The molecular fragments may help for the assessment of the fu,p values of different small molecules/drugs in a speedy way in comparison to experiment-based in vivo and in vitro studies.

Successfully navigating the valley of death: the importance of accelerators to support academic drug discovery and development

INTRODUCTION

The drug discovery and development 'valley of death' remains a challenge for promising new therapies originating from academic research laboratories. Drug discovery support centers and accelerators have been established to provide monetary and scientific support, but limited available funding along with cultural and expertise gaps remain obstacles for many promising technologies.

AREAS COVERED

In this meta-opinion article, the authors summarize the literature around obstacles that academic drug discovery projects face, along with potential solutions and best practices. Topics covered include funding challenges, regulatory education, reproducibility, along with cultural and organizational considerations. It describes one accelerator in particular-Critical Path Institute's Translational Therapeutics Accelerator (TRxA)-that aims to overcome several of the mentioned challenges.

EXPERT OPINION

The 'valley of death' remains a stubborn but not insurmountable part of the academic drug discovery and development landscape. Purposely designed accelerators can help, complementing more traditional intra- and extramural funding support.

Synthetic GPR40/FFAR1 agonists: An exhaustive survey on the most recent chemical classes and their structure-activity relationships

Free fatty acid receptor 1 (FFAR1 or GPR40) is a potential target for treating type 2 diabetes mellitus (T2DM) and related disorders that have been extensively researched for many years. GPR40/FFAR1 is a promising anti-diabetic target because it can activate insulin, promoting glucose metabolism. It controls T2DM by regulating glucose levels in the body through two separate mechanisms: glucose-stimulated insulin secretion and incretin production. In the last few years, various synthetic GPR40/FFAR1 agonists have been discovered that fall under several chemical classes, viz. phenylpropionic acid, phenoxyacetic acid, and dihydrobenzofuran acetic acid. However, only a few synthetic agonists have entered clinical trials due to various shortcomings like poor efficacy, low lipophilicity and toxicity issues. As a result, pharmaceutical firms and research institutions are interested in developing synthetic GPR40/FFAR1 agonists with superior effectiveness, lipophilicity, and safety profiles. This review encompasses the most recent research on synthetic GPR40/FFAR1 agonists, including their chemical classes, design strategies and structure-activity relationships. Additionally, we have emphasised the structural characteristics of the most potent GPR40/FFAR1 agonists from each chemical class of synthetic derivatives and analysed their chemico-biological interactions. This work will hopefully pave the way for developing more potent and selective synthetic GPR40/FFAR1 agonists for treating T2DM and related disorders.

Antibiotics with novel mode of action as new weapons to fight antimicrobial resistance

Antimicrobial resistance (AMR) is a major public health issue, causing 5 million deaths per year. Without any action plan, AMR will be in a near future the leading cause of death ahead of cancer. AMR comes from the ability of bacteria to rapidly develop and share resistance mechanisms towards current antibiotics, rendering them less effective. To circumvent this issue and avoid the phenomenon of cross-resistance, new antibiotics acting on novel targets or with new modes of action are required. Today, the pipeline of potential new treatments with these characteristics includes promising compounds such as gepotidacin, zoliflodacin, ibezapolstat, MGB-BP-3, CRS-3123, afabicin and TXA-709, which are currently in clinical trials, and lefamulin, which has been recently approved by FDA and EMA. In this review, we report the chemical synthesis, mode of action, structure-activity relationships, in vitro and in vivo activities as well as clinical data of these eight small molecules listed above.

Optimization of Cavity-Based Negative Images to Boost Docking Enrichment in Virtual Screening

Molecular docking is a key in silico method used routinely in modern drug discovery projects. Although docking provides high-quality ligand binding predictions, it regularly fails to separate the active compounds from the inactive ones. In negative image-based rescoring (R-NiB), the shape/electrostatic potential (ESP) of docking poses is compared to the negative image of the protein's ligand binding cavity. While R-NiB often improves the docking yield considerably, the cavity-based models do not reach their full potential without expert editing. Accordingly, a greedy search-driven methodology, brute force negative image-based optimization (BR-NiB), is presented for optimizing the models via iterative editing and benchmarking. Thorough and unbiased training, testing and stringent validation with a multitude of drug targets, and alternative docking software show that BR-NiB ensures excellent docking efficacy. BR-NiB can be considered as a new type of shape-focused pharmacophore modeling, where the optimized models contain only the most vital cavity information needed for effectively filtering docked actives from the inactive or decoy compounds. Finally, the BR-NiB code for performing the automated optimization is provided free-of-charge under MIT license via GitHub (https://github.com/jvlehtonen/brutenib) for boosting the success rates of docking-based virtual screening campaigns.

Monobodies as tool biologics for accelerating target validation and druggable site discovery

Despite increased investment and technological advancement, new drug approvals have not proportionally increased. Low drug approval rates, particularly for new targets, are linked to insufficient target validation at early stages. Thus, there remains a strong need for effective target validation techniques. Here, we review the use of synthetic binding proteins as tools for drug target validation, with focus on the monobody platform among several advanced synthetic binding protein platforms. Monobodies with high affinity and high selectivity can be rapidly developed against challenging targets, such as KRAS mutants, using protein engineering technologies. They have strong tendency to bind to functional sites and thus serve as drug-like molecules, and they can serve as targeting ligands for constructing bio-PROTACs. Genetically encoded monobodies are effective "tool biologics" for validating intracellular targets. They promote crystallization and help reveal the atomic structures of the monobody-target interface, which can inform drug design. Using case studies, we illustrate the potential of the monobody technology in accelerating target validation and small-molecule drug discovery.

Improving Translational Paradigms in Drug Discovery and Development

Despite improved knowledge regarding disease causality, new drug targets, and enabling technologies, the attrition rate for compounds entering clinical trials has remained consistently high for several decades, with an average 90% failure rate. These failures are manifested in an inability to reproduce efficacy findings from animal models in humans and/or the occurrence of unexpected safety issues, and reflect failures in T1 translation. Similarly, an inability to sequentially demonstrate compound efficacy and safety in Phase IIa, IIb, and III clinical trials represents failures in T2 translation. Accordingly, T1 and T2 translation are colloquially termed 'valleys of death'. Since T2 translation dealt almost exclusively with clinical trials, T3 and T4 translational steps were added, with the former focused on facilitating interactions between laboratory- and population-based research and the latter on 'real world' health outcomes. Factors that potentially lead to T1/T2 compound attrition include: the absence of biomarkers to allow compound effects to be consistently tracked through development; a lack of integration/'de-siloing' of the diverse discipline-based and technical skill sets involved in drug discovery; the industrialization of drug discovery, which via volume-based goals often results in quantity being prioritized over quality; inadequate project governance and strategic oversight; and flawed decision making based on unreliable/irreproducible or incomplete data. A variety of initiatives have addressed this problem, including the NIH National Center for Advancing Translational Sciences (NCATS), which has focused on bringing an unbiased academic perspective to translation, to potentially revitalize the process. This commentary provides an overview of the basic concepts involved in translation, along with suggested changes in the conduct of biomedical research to avoid valleys of death, including the use of Translational Scoring as a tool to avoid translational attrition and the impact of the FDA Accelerated Approval Pathway in lowering the hurdle for drug approval. © 2021 Wiley Periodicals LLC.

Chemsearch: collaborative compound libraries with structure-aware browsing

Summary

Chemsearch is a cross-platform server application for developing and managing a chemical compound library and associated data files, with an interface for browsing and search that allows for easy navigation to a compound of interest, similar compounds or compounds that have desired structural properties. With provisions for access control and centralized document and data storage, Chemsearch supports collaboration by distributed teams.

Availability and implementation

Chemsearch is a free and open-source Flask web application that can be linked to a Google Workspace account. Source code is available at https://github.com/gem-net/chemsearch (GPLv3 license). A Docker image allowing rapid deployment is available at https://hub.docker.com/r/cgemcci/chemsearch.

In Silico Prediction of Fraction Unbound in Human Plasma from Chemical Fingerprint Using Automated Machine Learning

Predicting the fraction unbound of a drug in plasma plays a significant role in understanding its pharmacokinetic properties during in vitro studies of drug design and discovery. Owing to the gaining reliability of machine learning in biological predictive models and development of automated machine learning techniques for the ease of nonexperts of machine learning to optimize and maximize the reliability of the model, in this experiment, we built an in silico prediction model of a fraction unbound drug in human plasma using a chemical fingerprint and a freely available AutoML framework. The predictive model was trained on one of the largest data sets ever of 5471 experimental values using four different AutoML frameworks to compare their performance on this problem and to choose the most significant one. With a coefficient of determination of 0.85 on the test data set, our best prediction model showed better performance than other previously published models, giving our model significant importance in pharmacokinetic modeling.

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.

Natural Products as Modulators of Sirtuins

Natural products have been used for the treatment of human diseases since ancient history. Over time, due to the lack of precise tools and techniques for the separation, purification, and structural elucidation of active constituents in natural resources there has been a decline in financial support and efforts in characterization of natural products. Advances in the design of chemical compounds and the understanding of their functions is of pharmacological importance for the biomedical field. However, natural products regained attention as sources of novel drug candidates upon recent developments and progress in technology. Natural compounds were shown to bear an inherent ability to bind to biomacromolecules and cover an unparalleled chemical space in comparison to most libraries used for high-throughput screening. Thus, natural products hold a great potential for the drug discovery of new scaffolds for therapeutic targets such as sirtuins. Sirtuins are Class III histone deacetylases that have been linked to many diseases such as Parkinson`s disease, Alzheimer’s disease, type II diabetes, and cancer linked to aging. In this review, we examine the revitalization of interest in natural products for drug discovery and discuss natural product modulators of sirtuins that could serve as a starting point for the development of isoform selective and highly potent drug-like compounds, as well as the potential application of naturally occurring sirtuin inhibitors in human health and those in clinical trials.

Academic Discovery of Anticancer Drugs: Historic and Future Perspectives

The identification and prosecution of meritorious anticancer drug targets and the discovery of clinical candidates represent an extraordinarily time- and resource-intensive process, and the current failure rate of late-stage drugs is a critical issue that must be addressed. Relationships between academia and industry in drug discovery and development have continued to change over time as a result of technical and financial challenges and, most importantly, to the objective of translating impactful scientific discoveries into clinical opportunities. This Golden Age of anticancer drug discovery features an increased appreciation for the high-risk, high-innovation research conducted in the nonprofit sector, with the goals of infusing commercial drug development with intellectual capital and curating portfolios that are financially tenable and clinically meaningful. In this review, we discuss the history of academic-industry interactions in the context of antidrug discovery and offer a view of where these interactions are likely headed as we continue to reach new horizons in our understanding of the immense complexities of cancer biology.

The transition to magic bullets - transition state analogue drug design

In the absence of industry partnerships, most academic groups lack the infrastructure to rationally design and build drugs via methods used in industry. Instead, academia needs to work smarter using mechanism-based design. Working smarter can mean the development of new drug discovery paradigms and then demonstrating their utility and reproducibility to industry. The collaboration between Vern Schramm's group at the Albert Einstein College of Medicine, USA and Peter Tyler at the Ferrier Research Institute at The Victoria University of Wellington, NZ has refined a drug discovery process called transition state analogue design. This process has been applied to several biomedically relevant nucleoside processing enzymes. In 2017, Mundesine®, conceived using transition state analogue design, received market approval for the treatment of peripheral T-cell lymphoma in Japan. This short review looks at a brief history of transition state analogue design, the fundamentals behind the development of this process, and the success of enzyme inhibitors produced using this drug design methodology.

Predicting Fraction Unbound in Human Plasma from Chemical Structure: Improved Accuracy in the Low Value Ranges

Predicting the fraction unbound in plasma provides a good understanding of the pharmacokinetic properties of a drug to assist candidate selection in the early stages of drug discovery. It is also an effective tool to mitigate the risk of late-stage attrition and to optimize further screening. In this study, we built in silico prediction models of fraction unbound in human plasma with freely available software, aiming specifically to improve the accuracy in the low value ranges. We employed several machine learning techniques and built prediction models trained on the largest ever data set of 2738 experimental values. The classification model showed a high true positive rate of 0.826 for the low fraction unbound class on the test set. The strongly biased distribution of the fraction unbound in plasma was mitigated by a logarithmic transformation in the regression model, leading to improved accuracy at lower values. Overall, our models showed better performance than those of previously published methods, including commercial software. Our prediction tool can be used on its own or integrated into other pharmacokinetic modeling systems.

The development of ruthenium(ii) polypyridyl complexes and conjugates for in vitro cellular and in vivo applications

Ruthenium(ii) [Ru(ii)] polypyridyl complexes have been the focus of intense investigations since work began exploring their supramolecular interactions with DNA. In recent years, there have been considerable efforts to translate this solution-based research into a biological environment with the intention of developing new classes of probes, luminescent imaging agents, therapeutics and theranostics. In only 10 years the field has expanded with diverse applications for these complexes as imaging agents and promising candidates for therapeutics. In light of these efforts this review exclusively focuses on the developments of these complexes in biological systems, both in cells and in vivo, and hopes to communicate to readers the diversity of applications within which these complexes have found use, as well as new insights gained along the way and challenges that researchers in this field still face.

How good are publicly available web services that predict bioactivity profiles for drug repurposing?

Drug repurposing provides a non-laborious and less expensive way for finding new human medicines. Computational assessment of bioactivity profiles shed light on the hidden pharmacological potential of the launched drugs. Currently, several freely available computational tools are available via the Internet, which predict multitarget profiles of drug-like compounds. They are based on chemical similarity assessment (ChemProt, SuperPred, SEA, SwissTargetPrediction and TargetHunter) or machine learning methods (ChemProt and PASS). To compare their performance, this study has created two evaluation sets, consisting of (1) 50 well-known repositioned drugs and (2) 12 drugs recently patented for new indications. In the first set, sensitivity values varied from 0.64 (TarPred) to 1.00 (PASS Online) for the initial indications and from 0.64 (TarPred) to 0.98 (PASS Online) for the repurposed indications. In the second set, sensitivity values varied from 0.08 (SuperPred) to 1.00 (PASS Online) for the initial indications and from 0.00 (SuperPred) to 1.00 (PASS Online) for the repurposed indications. Thus, this analysis demonstrated that the performance of machine learning methods surpassed those of chemical similarity assessments, particularly in the case of novel repurposed indications.

Choose and Use Your Chemical Probe Wisely to Explore Cancer Biology

Small-molecule chemical probes or tools have become progressively more important in recent years as valuable reagents to investigate fundamental biological mechanisms and processes causing disease, including cancer. Chemical probes have also achieved greater prominence alongside complementary biological reagents for target validation in drug discovery. However, there is evidence of widespread continuing misuse and promulgation of poor-quality and insufficiently selective chemical probes, perpetuating a worrisome and misleading pollution of the scientific literature. We discuss current challenges with the selection and use of chemical probes, and suggest how biologists can and should be more discriminating in the probes they employ.

Curcumin alleviates lumbar radiculopathy by reducing neuroinflammation, oxidative stress and nociceptive factors

Current non-surgical treatments for lumbar radiculopathy [e.g. epidural steroids and Tumour necrosis factor-α (TNF-α) antagonists] are neither effective nor safe. As a non-toxic natural product, curcumin possesses an exceptional anti-inflammatory profile. We hypothesised that curcumin alleviates lumbar radiculopathy by attenuating neuroinflammation, oxidative stress and nociceptive factors. In a dorsal root ganglion (DRG) culture, curcumin effectively inhibited TNF-α-induced neuroinflammation, in a dose-dependent manner, as shown by mRNA and protein expression of IL-6 and COX-2. Such effects might be mediated via protein kinase B (AKT) and extracellular signal regulated kinase (ERK) pathways. Also, a similar effect in combating TNF-α-induced neuroinflammation was observed in isolated primary neurons. In addition, curcumin protected neurons from TNF-α-triggered excessive reactive oxygen species (ROS) production and cellular apoptosis and, accordingly, promoted mRNA expression of the anti-oxidative enzymes haem oxygenase-1, catalase and superoxide dismutase-2. Intriguingly, electronic von Frey test suggested that intraperitoneal injection of curcumin significantly abolished ipsilateral hyperalgesia secondary to disc herniation in mice, for up to 2 weeks post-surgery. Such in vivo pain alleviation could be attributed to the suppression, observed in DRG explant culture, of TNF-α-elicited neuropeptides, such as substance P and calcitonin gene-related peptide. Surprisingly, micro-computed tomography (μCT) data suggested that curcumin treatment could promote disc height recovery following disc herniation. Alcian blue/picrosirius red staining confirmed that systemic curcumin administration promoted regeneration of extracellular matrix proteins, visualised by presence of abundant newly-formed collagen and proteoglycan content in herniated disc. Our study provided pre-clinical evidence for expediting this natural, non-toxic pleiotropic agent to become a new and safe clinical treatment of radiculopathy.

Industry–Academic Relationship in a New Era of Drug Discovery

The pharmaceutical industry is entering a renewed period of productivity as a result of advances in the understanding of human biology, particularly in the areas of genetics and immunology. The relationship between industry and academia needs to evolve to maximize the opportunity. In four areas—target identification, the molecule itself, experimental medicine, and larger-scale clinical testing—there are specific needs for academic partnerships that should be open and transparent and include talent, skills, and career development.

A Wide-Field Fluorescence Microscope Extension for Ultrafast Screening of One-Bead One-Compound Libraries Using a Spectral Image Subtraction Approach

The increasing involvement of academic institutions and biotech companies in drug discovery calls for cost-effective methods to identify new bioactive molecules. Affinity-based on-bead screening of combinatorial one-bead one-compound libraries combines a split-mix synthesis design with a simple protein binding assay operating directly at the bead matrix. However, one bottleneck for academic scale on-bead screening is the unavailability of a cheap, automated, and robust screening platform that still provides a quantitative signal related to the amount of target protein binding to individual beads for hit bead ranking. Wide-field fluorescence microscopy has long been considered unsuitable due to significant broad spectrum autofluorescence of the library beads in conjunction with low detection sensitivity. Herein, we demonstrate how such a standard microscope equipped with LED-based excitation and a modern CMOS camera can be successfully used for selecting hit beads. We show that the autofluorescence issue can be overcome by an optical image subtraction approach that yields excellent signal-to-noise ratios for the detection of bead-associated target proteins. A polymer capillary attached to a semiautomated bead-picking device allows the operator to efficiently isolate individual hit beads in less than 20 s. The system can be used for ultrafast screening of >200,000 bead-bound compounds in 1.5 h, thereby making high-throughput screening accessible to a wider group within the scientific community.

Development of cell therapy medicinal products by academic institutes

In the rapidly evolving fields of cellular immunotherapy, gene therapy and regenerative medicine, a wide range of promising cell therapy medicinal products are in clinical development. Most products originate from academic research and are explored in early exploratory clinical trials. However, the success rate toward approval for regular patient care is disappointingly low. In this paper, we define strengths and hurdles applying to the development of cell therapy medicinal products in academic institutes, and analyze why only a few promising cell therapies have reached late-stage clinical development. Subsequently, we provide recommendations to stakeholders involved in development of cell therapies to exploit their potential clinical benefit.

Academic-Industrial Collaboration: Toward the Consilience of Two Solitudes

Current major advances in drug discovery can be traced back to pioneering contributions originating from academics over a century ago. Living in a symbiotic yet noninvasive coexistence, the academic community and the pharmaceutical industry have strived, each in their own way, to develop the modern medicines that benefit humankind today. The subject is presented from a historical and personal perspective.

Academic drug discovery: current status and prospects

INTRODUCTION

The contraction in pharmaceutical drug discovery operations in the past decade has been counter-balanced by a significant rise in the number of academic drug discovery groups. In addition, pharmaceutical companies that used to operate in completely independent, vertically integrated operations for drug discovery, are now collaborating more with each other, and with academic groups. We are in a new era of drug discovery.

AREAS COVERED

This review provides an overview of the current status of academic drug discovery groups, their achievements and the challenges they face, together with perspectives on ways to achieve improved outcomes.

EXPERT OPINION

Academic groups have made important contributions to drug discovery, from its earliest days and continue to do so today. However, modern drug discovery and development is exceedingly complex, and has high failure rates, principally because human biology is complex and poorly understood. Academic drug discovery groups need to play to their strengths and not just copy what has gone before. However, there are lessons to be learnt from the experiences of the industrial drug discoverers and four areas are highlighted for attention: i) increased validation of targets; ii) elimination of false hits from high throughput screening (HTS); iii) increasing the quality of molecular probes; and iv) investing in a high-quality informatics infrastructure.

How to report and discuss ADME data in medicinal chemistry publications: in vitro data or in vivo extrapolations?

Early drug discovery projects often utilize data from ADME (absorption, distribution, metabolism, elimination) assays to benchmark data and guide discussion, rather than the predicted in vivo consequences of these data. Here, the two paradigms are compared, using evaluations of metabolic stability based on either microsomal clearance assay data or from the predicted in vivo hepatic clearance and half-life calculated through the combination of the venous well-stirred model and Øie-Tozer's model. The need for a shift in paradigm is presented, and its implications discussed. It is suggested that discussions about ADME data should revolve around potential clinical problems that are most likely to surface during the development phase, each benchmarked with a suitable variable derived from the assay data.

Visualization of plasma and tissue binding using dose fractions parameter

A novel concept of dose fractions, based on the distribution of total bioavailable dose between the six combinations of location and binding state in Øie-Tozer's model is suggested as a way to visualize the distribution pharmacokinetics of a drug. The concept of dose fractions provides a sharper terminology in discussions of drug distribution allowing for a more precise description of the state and location of a drug within a system. In medicinal chemistry literature, the free fraction of a drug in plasma is a commonly discussed factor affecting the exposure to free drug while tissue binding is less well addressed. The free dose fraction, defined as the fraction of the bioavailable dose existing in free form, is suggested as a potentially valuable term for such discussions. Presently, drugs with high (>95%) plasma protein binding are viewed with skepticism, the rational behind which is questioned. The plasma protein bound dose fraction defined as the fraction of the total available dose, which is bound to plasma proteins, is suggested as a measure of the risk for problems related to fluctuations in free drug exposure due to variations in the concentration of drug binding plasma protein.

A high content screening approach to identify molecules neuroprotective for photoreceptor cells

PURPOSE

Retinal degenerations are a heterogeneous group of diseases in which there is slow but progressive loss of photoreceptors (PR). There are currently no approved therapies for treating retinal degenerations. In an effort to identify novel small molecules that are (1) neuroprotective and (2) promote PR differentiation, we have developed microscale (1,536 well) cell culture assays using primary retinal neurons.

METHODS

Primary murine retinal cells are isolated, seeded, treated with a 1,280 compound chemical library in a 7 point titration and then cultured under conditions developed to assay protection against an introduced stress or enhance PR differentiation. In the protection assays a chemical insult is introduced and viability assessed after 72 h using CellTiterGlo, a single-step chemiluminescent reagent. In the differentiation assay, cells are isolated from the rhodopsin-GFP knock-in mouse and PR differentiation is assessed by fixing cells after 21 days in culture and imaging with the Acumen plate-based laser cytometer (TTP Labtech) to determine number and intensity of GFP-expressing cells. Positive wells are re-imaged at higher resolution with an INCell2000 automated microscope (GE). Concentration-response curves are generated to pharmacologically profile each compound and hits identified by xx.

RESULTS

We have developed PR differentiation and neuroprotection assays with a signal to background (S/B) ratios of 11 and 3, and a coefficient of variation (CV) of 20 and 9 %, suitable for chemical screening. Staurosporine has been shown in our differentiation assay to simultaneously increase the number of rhodopsin positive objects while decreasing the mean rhodopsin intensity and punctate rhodopsin fluorescent objects.

CONCLUSIONS

Using primary murine retinal cells, we developed high throughput assays to identify small molecules that influence PR development and survival. By screening multiple compound concentrations, dose-response curves can be generated, and the false negative rate minimized. It is hoped that this work will identify both potential preclinical candidates as well as molecular probes that will be useful for analysis of the molecular mechanisms that promote PR differentiation and survival.

The fall and rise of pharmacology--(re-)defining the discipline?

Pharmacology is an integrative discipline that originated from activities, now nearly 7000 years old, to identify therapeutics from natural product sources. Research in the 19th Century that focused on the Law of Mass Action (LMA) demonstrated that compound effects were dose-/concentration-dependent eventually leading to the receptor concept, now a century old, that remains the key to understanding disease causality and drug action. As pharmacology evolved in the 20th Century through successive biochemical, molecular and genomic eras, the precision in understanding receptor function at the molecular level increased and while providing important insights, led to an overtly reductionistic emphasis. This resulted in the generation of data lacking physiological context that ignored the LMA and was not integrated at the tissue/whole organism level. As reductionism became a primary focus in biomedical research, it led to the fall of pharmacology. However, concerns regarding the disconnect between basic research efforts and the approval of new drugs to treat 21st Century disease tsunamis, e.g., neurodegeneration, metabolic syndrome, etc. has led to the reemergence of pharmacology, its rise, often in the semantic guise of systems biology. Against a background of limited training in pharmacology, this has resulted in issues in experimental replication with a bioinformatics emphasis that often has a limited relationship to reality. The integration of newer technologies within a pharmacological context where research is driven by testable hypotheses rather than technology, together with renewed efforts in teaching pharmacology, is anticipated to improve the focus and relevance of biomedical research and lead to novel therapeutics that will contain health care costs.

Small-molecule inhibitors of IκB kinase (IKK) and IKK-related kinases

The transcription factors NF-κB and IFN control important signaling cascades and mediate the expression of a number of important pro-inflammatory cytokines, adhesion molecules, growth factors and anti-apoptotic survival proteins. IκB kinase (IKK) and IKK-related kinases (IKKε and TBK1) are key regulators of these biological pathways and, as such, modulators of these enzymes may be useful in the treatment of inflammatory diseases and cancer. We have reviewed the most recent IKK patent literature (2008–2012), added publications of interest overlooked in previous patent reviews and identified all the players involved in small-molecule inhibitors of the IKKs. This will provide the reader with a decisive summary of the IKK arena, a field that has reached maturity over a decade of research.

NIPTE: a multi-university partnership supporting academic drug development

The strategic goal of academic translational research is to accelerate translational science through the improvement and development of resources for moving discoveries across translational barriers through 'first in humans' studies. To achieve this goal, access to drug discovery resources and preclinical IND-enabling infrastructure is crucial. One potential approach of research institutions for coordinating preclinical development, based on a model from the National Institute for Pharmaceutical Technology and Education (NIPTE), can provide academic translational and medical centers with access to a wide variety of enabling infrastructure for developing small molecule clinical candidates in an efficient, cost-effective manner.

Collaboration versus outsourcing: the need to think outside the box

As has been widely reviewed elsewhere, the pharmaceutical industry is experiencing an ‘innovation deficit’ as evidenced by the decline in new chemical entity output. This decline, compounded by increased costs and regulatory requirements highlights the need to significantly revise strategic options across the drug-discovery spectrum. Within such revision(s), much of the focus has been on outsourcing to reduce, or at least contain, costs, but if the underlying predominance of ‘closed collaborations’ is not challenged to allow better use of combined knowledge and, thus, move towards a more genuine collaborative process then a ‘numbers only’ approach will not bring medium-to-long-term survival. There are many problems to confront in evolving new sustainable strategies, a real need to think differently exists and should to be cultivated. This article reviews current outsourcing and collaboration strategies to provide a perspective on how great knowledge sharing could help revise the drug-discovery process.