Scientific process, pharmacology and drug discovery

Generation of new inhibitors of selected cytochrome P450 subtypes- In silico study

Physicochemical and pharmacokinetic compound profile has crucial impact on compound potency to become a future drug. Ligands with desired activity profile cannot be used for treatment if they are characterized by unfavourable physicochemical or ADMET properties. In the study, we consider metabolic stability and focus on selected subtypes of cytochrome P450 - proteins, which take part in the first phase of compound transformations in the organism. We develop a protocol for generation of new potential inhibitors of selected cytochrome isoforms. Its subsequent stages are composed of generation and assessment of new derivatives of known cytochrome inhibitors, docking and evaluation of the compound possible inhibition on the basis of the obtained ligand-protein complexes. Besides the library of new potential agents inhibiting particular cytochrome subtypes, we also prepare a graph neural network that predicts the change in activity for all modifications of the starting molecule. In addition, we perform a systematic statistical study on the influence of particular substitutions on the potential inhibition properties of generated compounds (both mono- and di-substitutions are considered), provide explanations of the inhibitory predictions and prepare an on-line visualization platform enabling manual inspection of the results. The developed methodology can greatly support the design of new cytochrome P450 inhibitors with the overarching goal of generation of new metabolically stable compounds. It enables instant evaluation of possible compound-cytochrome interactions and selection of ligands with the highest potential of possessing desired biological activity.

Recent advances in human respiratory epithelium models for drug discovery

The respiratory epithelium is intimately associated with the pathophysiologies of highly infectious viral contagions and chronic illnesses such as chronic obstructive pulmonary disorder, presently the third leading cause of death worldwide with a projected economic burden of £1.7 trillion by 2030. Preclinical studies of respiratory physiology have almost exclusively utilised non-humanised animal models, alongside reductionistic cell line-based models, and primary epithelial cell models cultured at an air-liquid interface (ALI). Despite their utility, these model systems have been limited by their poor correlation to the human condition. This has undermined the ability to identify novel therapeutics, evidenced by a 15% chance of success for medicinal respiratory compounds entering clinical trials in 2018. Consequently, preclinical studies require new translational efficacy models to address the problem of respiratory drug attrition. This review describes the utility of the current in vivo (rodent), ex vivo (isolated perfused lungs and precision cut lung slices), two-dimensional in vitro cell-line (A549, BEAS-2B, Calu-3) and three-dimensional in vitro ALI (gold-standard and co-culture) and organoid respiratory epithelium models. The limitations to the application of these model systems in drug discovery research are discussed, in addition to perspectives of the future innovations required to facilitate the next generation of human-relevant respiratory models.

Direct label-free methods for identification of target proteins in agrochemicals

Green agrochemicals are important guarantee for food production and security, and target protein identification is the most important basis for development of novel agrochemicals. Affinity chromatography methods for immobilization of agrochemicals have been widely used to identify and confirm new targets. However, this method often requires modification of the active molecules which can affect or damage its biological activity, and biomacromolecules, particularly most natural products, are hard to be modified either. In order to overcome the shortcomings of molecular modification, label-free technology has been developed based on evaluating responses to thermal or proteolytic treatments. Combined with the chemical biology technology and molecular biology technology, it has been used in the development of drugs and agrochemicals. Herein, common methods of label-free technology for identification of direct target of agrochemicals are reviewed, including the principle, advantages, limitations and applications in the research of agrochemicals in the last decade. And the methods for validation of candidate targets obtained by the label-free methods are also reviewed, which are important to obtain the accurate and reliable targets. Combined application of these methods will greatly reduce the experimental costs and shorten the period for the new target identification and validation by improving its accuracy, which will provide a systematic solution for new ecological agrochemicals research and development.

Optimising experimental research in respiratory diseases: an ERS statement

Experimental models are critical for the understanding of lung health and disease and are indispensable for drug development. However, the pathogenetic and clinical relevance of the models is often unclear. Further, the use of animals in biomedical research is controversial from an ethical perspective.The objective of this task force was to issue a statement with research recommendations about lung disease models by facilitating in-depth discussions between respiratory scientists, and to provide an overview of the literature on the available models. Focus was put on their specific benefits and limitations. This will result in more efficient use of resources and greater reduction in the numbers of animals employed, thereby enhancing the ethical standards and translational capacity of experimental research.The task force statement addresses general issues of experimental research (ethics, species, sex, age, ex vivo and in vitro models, gene editing). The statement also includes research recommendations on modelling asthma, chronic obstructive pulmonary disease, pulmonary fibrosis, lung infections, acute lung injury and pulmonary hypertension.The task force stressed the importance of using multiple models to strengthen validity of results, the need to increase the availability of human tissues and the importance of standard operating procedures and data quality.

Active machine learning-driven experimentation to determine compound effects on protein patterns

High throughput screening determines the effects of many conditions on a given biological target. Currently, to estimate the effects of those conditions on other targets requires either strong modeling assumptions (e.g. similarities among targets) or separate screens. Ideally, data-driven experimentation could be used to learn accurate models for many conditions and targets without doing all possible experiments. We have previously described an active machine learning algorithm that can iteratively choose small sets of experiments to learn models of multiple effects. We now show that, with no prior knowledge and with liquid handling robotics and automated microscopy under its control, this learner accurately learned the effects of 48 chemical compounds on the subcellular localization of 48 proteins while performing only 29% of all possible experiments. The results represent the first practical demonstration of the utility of active learning-driven biological experimentation in which the set of possible phenotypes is unknown in advance.

DOI:http://dx.doi.org/10.7554/eLife.10047.001

Biomedical scientists have invested significant effort into making it easy to perform lots of experiments quickly and cheaply. These “high throughput” methods are the workhorses of modern “systems biology” efforts. However, we simply cannot perform an experiment for every possible combination of different cell type, genetic mutation and other conditions. In practice this has led researchers to either exhaustively test a few conditions or targets, or to try to pick the experiments that best allow a particular problem to be explored. But which experiments should we pick? The ones we think we can predict the outcome of accurately, the ones for which we are uncertain what the results will be, or a combination of the two?

Humans are not particularly well suited for this task because it requires reasoning about many possible outcomes at the same time. However, computers are much better at handling statistics for many experiments, and machine learning algorithms allow computers to “learn” how to make predictions and decisions based on the data they’ve previously processed.

Previous computer simulations showed that a machine learning approach termed “active learning” could do a good job of picking a series of experiments to perform in order to efficiently learn a model that predicts the results of experiments that were not done. Now, Naik et al. have performed cell biology experiments in which experiments were chosen by an active learning algorithm and then performed using liquid handling robots and an automated microscope. The key idea behind the approach is that you learn more from an experiment you can’t predict (or that you predicted incorrectly) than from just confirming your confident predictions.

The results of the robot-driven experiments showed that the active learning approach outperforms strategies a human might use, even when the potential outcomes of individual experiments are not known beforehand. The next challenge is to apply these methods to reduce the cost of achieving the goals of large projects, such as The Cancer Genome Atlas.

DOI:http://dx.doi.org/10.7554/eLife.10047.002

Advances in asthma drug discovery: evaluating the potential of nasal cell sampling and beyond

INTRODUCTION

Inhaled corticosteroid anti-inflammatory therapy is effective at controlling disease symptoms of asthma, but a subset of patients remains symptomatic despite optimal treatment, creating a clear unmet medical need. Moreover, none of the currently available drugs for asthma are really disease-modifying or curative. Although murine models of asthma, based on transgenic and knockout animals, may offer an integrated pathophysiological system for studying the characteristics of airway inflammation and hyperresponsiveness, these alterations are noteworthily different compared with those observed in asthmatic patients. Since a clear functional and inflammatory relationship between the nasal mucosa and bronchial tissue in patients suffering from asthma and allergic rhinitis has been recognized, using preclinical models based on human nasal cells sampling might support a prompt and effective anti-inflammatory drug discovery in asthma.

AREAS COVERED

The authors provide a review, which discusses the potential role of nasal cell sampling and its application in advanced drug discovery for asthma. The contents range from the similarities and differences between asthma and allergic rhinitis up to artificial airway models based on sophisticated human lung-on-a-chip devices.

EXPERT OPINION

Nasal cell sampling and processing have reached a great potential in asthma drug discovery. The authors believe that models of asthma, which are based on human nasal cells, can provide valuable indications of proof of pharmacological and potential therapeutic efficacy in both preclinical and early clinical settings.

Clinical pharmacology in neuroscience drug discovery: quo vadis?

Clinical Pharmacology in Neuroscience Drug Discovery in recent years has concentrated on First Time in Human safety and pharmacokinetics. The more traditional pharmacological research in humans has been reduced mainly as a response to the difficulty of developing human pharmacology models in neuroscience diseases. As a consequence, opportunities are being missed to aid in target selection and in target validation. The decision of big Pharma to reduce investment from the Neurosciences has had implications for clinical pharmacologists in this area. It remains to be seen whether academia, government laboratories and contract houses will respond to the challenge of carrying out increased Clinical Pharmacology in the Neurosciences.

Nano bioresearch approach by microtechnology

To progress in basic science and drug development, convenient methodology for detecting specific biological molecules and their interaction in living organism is in high demand. After more than 20 years of increasing research efforts, micro and nanotechnologies are now mature to propose a new class of miniature devices and principles enabling compartmentalized bioassays. Among them, this review proposes various examples that include array of electro-active microwells for highly parallel single cell analysis, cost-effective nanofluidic for DNA separation, parallel enzymatic reaction in 100pL droplet and high-throughput platform for membrane proteins assays. The micro devices are presented with relevant experiments to foresee their future contribution to translational research and drug discovery.

The central role of chemistry in ‘quality by design’ approaches to drug development

The quality of medicines reaching the consumer is strictly controlled and maintained by the regulatory agencies of the world. Pharmaceutical companies have to meet and maintain these regulatory quality standards. For this purpose, an increasing number of processes are incorporating quality by design (QbD) principles. Implementation of QbD involves chemistry in several ways, such as in the development of new synthetic and analytical methods, avoiding formation of genotoxic impurities and designing drug-like compounds to improve the quality of biological profile of medicines. A combined effort from regulatory authorities, pharmaceutical industries and academic research groups could also facilitate QbD implementation.

Mass spectrometric analysis of prefrontal cortex proteins in schizophrenia and bipolar disorder

BACKGROUND

Schizophrenia and bipolar disorder are the two most serious and debilitating neuropsychiatric disorders that share many characteristics, both symptomatic and epidemiological. There has yet to be a single diagnostic biomarker discovered for schizophrenia and bipolar disorder. Proteomics holds promise in elucidating the pathophysiology of these neuropsychiatric disorders from each other and healthy individuals.

FINDINGS

Postmortem prefrontal cortex tissue from schizophrenia, bipolar disorder, and psychiatric-free controls (n = 35 in each group) were subject to SELDI-TOF-MS protein profiling. There were 13 protein peaks distinguishing schizophrenia versus control and 15 in bipolar versus control. Using a predictor set of 10 peaks for each comparison, 73% prediction accuracy (p = 2.3×10(-4)) was achieved. Three peaks were in common between schizophrenia and bipolar disorder.

CONCLUSIONS

This pilot study found protein profiles that distinguished schizophrenia and bipolar patients from controls and notably from each other. Identifying and characterizing the proteins in this study may elucidate neuropsychiatric phenotypes and uncover therapeutic targets. Further, applying class prediction bioinformatics may allow the clinician to differentiate the two phenotypes by profiling CSF or even serum.