In vitro primary human and animal cell-based blood-brain barrier models as a screening tool in drug discovery

Brain penetration is characterized by its extent and rate and is influenced by drug physicochemical properties, plasma exposure, plasma and brain protein binding and BBB permeability. This raises questions related to physiology, interspecies differences and in vitro/in vivo extrapolation. We herein discuss the use of in vitro human and animal BBB model as a tool to improve CNS compound selection. These cell-based BBB models are characterized by low paracellular permeation, well-developed tight junctions and functional efflux transporters. A study of twenty drugs shows similar compound ranking between rat and human models although with a 2-fold higher permeability in rat. cLogP < 5, PSA < 120 ?, MW < 450 were confirmed as essential for CNS drugs. An in vitro/in vivo correlation in rat (R2 = 0.67; P = 2 × 10??) was highlighted when in vitro permeability and efflux were considered together with plasma exposure and free fraction. The cell-based BBB model is suitable to optimize CNS-drug selection, to study interspecies differences and then to support human brain exposure prediction.     

Chemical microarray: a new tool for drug screening and discovery

HTS with microtiter plates has been the major tool used in the pharmaceutical industry to explore chemical diversity space and to identify active compounds and pharmacophores for specific biological targets. However, HTS faces a daunting challenge regarding the fast-growing numbers of drug targets arising from genomic and proteomic research, and large chemical libraries generated from high-throughput synthesis. There is an urgent need to find new ways to profile the activity of large numbers of chemicals against hundreds of biological targets in a fast, low-cost fashion. Chemical microarray can rise to this challenge because it has the capability of identifying and evaluating small molecules as potential therapeutic reagents. During the past few years, chemical microarray technology, with different surface chemistries and activation strategies, has generated many successes in the evaluation of chemical-protein interactions, enzyme activity inhibition, target identification, signal pathway elucidation and cell-based functional analysis. The success of chemical microarray technology will provide unprecedented possibilities and capabilities for parallel functional analysis of tremendous amounts of chemical compounds.     

Selecting oral bioavailability enhancing formulations during drug discovery and development

Introduction: The role of chemical structure, lipophilicity, physico-chemical, absorption, distribution, metabolism, excretion, toxicity (ADMET) and biopharmaceutical properties of compounds including bioavailability are critical in drug discovery and drug dosage forms design.

Areas covered: The authors discuss a number of parameters including computational approaches used for selected chemical structures with biological activity for lead optimization and chemogenomics and preclinical studies for ADMET process development of ligand properties. The authors also look at a number of other parameters including: early drug product formulations with method selection based on the biopharmaceutical classification system (BCS); in vitro–in vivo correlation (IVIVC) and different formulation strategies to enhance solubility; dissolution rate and permeability; bioavailability evaluation and quality by design as an opportunity to develop ‘safe space' regions, where bioavailability is unaffected by pharmaceutical variations.

Expert opinion: The biopharmaceutical requirements for absorption are solubility and permeability. Both are influenced by lipophilicity, but in the opposite way. The genomic methodology, coupled with combinatorial chemistry, high-throughput screening, structure-based design and in silico ADMET would yield parameters as a starting point for the biopharmaceutical properties determination in further preclinical and clinical studies. Consecutive stages in drug discovery and development are irreplaceable, but pharmacokinetics is the critical step. Selection of drug formulations based on the BCS, IVIVC are the principal aspects to enhance the solubility and dissolution rate, while a rationale management of pharmaceutical and technological factors will enhance the bioavailability.     

Random mutagenesis in the mouse as a tool in drug discovery

The flood of raw information generated by large-scale data acquisition technologies in genomics, microarrays and proteomics is changing the early stages of the drug discovery process. Although many more potential drug targets are now available compared with the pre-genomics era, knowledge about the physiological context in which these targets act--information crucial to both discovery and development--is scarce. Random mutagenesis strategies in the mouse provide scalable approaches for both the gene-driven validation of candidate targets in vivo and the discovery of new physiological pathways by phenotype-driven screens.     

Early discovery drug screening using mass spectrometry

Electrospray ionization (ESI) and matrix-assisted laser desorption/ionization (MALDI) mass spectrometric methods useful for early discovery drug screening are reviewed. All methods described involve studies of non-covalent complexes between biopolymer receptors and small molecule ligands formed in the condensed phase. The complexes can be sprayed intact directly into the gas phase by ESI-MS using gentle experimental conditions. Gas phase screening applications are illustrated for drug ligand candidates non-covalently interacting with peptides, proteins, RNA, and DNA. In the condensed phase, the complexes can be also isolated, denatured and analyzed by ESI-MS to identify the small molecule ligands. Condensed phase drug screening examples are illustrated for the ESI-MS ancillary techniques of affinity chromatography, ultrafiltration, ultracentrifugation, gel permeation chromatography (GPC), reverse phase-high performance liquid chromatography (RP-HPLC) and capillary electrophoretic methods. Solid phase drug screening using MALDI-MS is illustrated for small molecule ligands bound to MALDI affinity probe tips and to beads. Since ESI and MALDI principally produce molecular ions, high throughput screening is achieved by analyzing mass indexed mixtures.     

Stem cells as screening tools in drug discovery

All physiologic processes operate in a cellular setting. Therefore, drug discoverers need the highest quality cells as they pursue the next generation of safe and effective medicines. Recently, investigators have begun to consider stem cells as a new source of predictive, cell-based assays in drug discovery. Stem cell technology still has hurdles to overcome before these cells are fully accepted as decision-making reagents and amenable to high-throughput screening. However, with global research interest in stem cell biology, significant advances in the application of these cells in drug discovery have been reported. These advances are aligned with three important stages of pharmaceutical research: target discovery and validation, identification of efficacious chemical leads, and drug safety pharmacology. This concise review describes the application of stem cells in these areas of drug discovery with emphasis on molecular screening opportunities.     

Predicting in vivo drug interactions from in vitro drug discovery data

In vitro screening for drugs that inhibit cytochrome P450 enzymes is well established as a means for predicting potential metabolism-mediated drug interactions in vivo. Given that these predictions are based on enzyme kinetic parameters observed from in vitro experiments, the miscalculation of the inhibitory potency of a compound can lead to an inaccurate prediction of an in vivo drug interaction, potentially precluding a safe drug from advancing in development or allowing a potent inhibitor to 'slip' into the patient population. Here, we describe the principles underlying the generation of in vitro drug metabolism data and highlight commonly encountered uncertainties and sources of bias and error that can affect extrapolation of drug-drug interaction information to the clinical setting.     

Application of porcine gastrointestinal organoid units as a potential in vitro tool for drug discovery and development

The gastrointestinal tract (GI) is a crucial part of the body for growth and development and its dysregulation can lead to several diseases with detrimental effects. Most of these diseases lack effective treatment, occurring as a result of inappropriate models to develop safe and potent therapies. Organoids are three‐dimensional self‐organizing and self‐renewing structures that are composed of a cluster of different cells in vitro that resemble their organ of origin in architecture and function. Over recent years, organoids have been increasingly used to study developmental biology, disease progression, i.e., cancer, tissue engineering and regenerative medicine and other biological processes. Owing to their complex nature and ability to retain the morphological and molecular patterns of their tissue‐of‐origin, they have great potential as alternative tools/models for drug screening, development and biomarker discovery. Using a species with similar genetic homology to humans as a source of organoids, such as the porcine model may offer huge translational relevance. This review focuses on the culture and establishment of porcine organoid units and their potential use and application as in vitro models to further the science of drug discovery, by overcoming current limitations of established two‐ and three‐dimensional models. It also highlights the translational application of using porcine organoids as a model of different disease contexts.     

Maternal vaccination and preterm birth: using data mining as a screening tool

OBJECTIVE: The main purpose of this study was to identify possible associations between medicines used in pregnancy and preterm deliveries using data mining as a screening tool. SETTINGS: Prospective cohort study. METHODS: We used data mining to identify possible correlates between preterm delivery and medicines used by 92,235 pregnant Danish women who took part in the Danish National Birth Cohort (DNBC). We then evaluated the association between one of the identified exposures (vaccination) and the risk for preterm birth by using logistic regression. The women were classified into groups according to their exposure to vaccination. The regression analyses were adjusted for the following covariates: parity, infant's gender, maternal Body-Mass Index (BMI), age, smoking, drinking, job, number of inhabitants in the place of residence, infections, diabetes, high blood pressure and preeclampsia. MAIN OUTCOME MEASURE: Preterm birth, a delivery occurring before the 259th day of gestation (i.e., less than 37 full weeks). RESULTS: Data mining had indicated that maternal vaccination (among other factors) might be related to preterm birth. The following regression analysis showed that, the women who reported being vaccinated shortly before or during gestation had a slightly higher risk of giving preterm birth (O.R. = 1.14; 95% CI 1.04-1.25) as compared to the non-vaccinated group. CONCLUSION: Whether the association between maternal vaccination and the risk for preterm birth found here is causal or not deserves further studies. Data mining, especially with additional refinements, may be a valuable and very efficient tool to screen large databases for relevant information which can be used in clinical and public health research.     

Label-free kinetic binding data as a decisive element in drug discovery

The emerging possibilities to obtain label-free, kinetic-based binding data for drug–target and drug absorption, distribution, metabolism and excretion (ADME)–marker interactions have proven useful in many drug discovery related issues. Multiple reports have demonstrated that the common use of affinity as an early measure of drug potency may be directly misleading. This review summarises findings in the literature related to compound selection in the drug discovery process. It is important to understand the different properties of association and dissociation rates, the former being related to both structure and dosage and the latter depending solely on molecular structure. By performing parallel optimisations of association and dissociation rates, compounds with desirable kinetic profiles for target binding may be generated. In addition, compound selection may also consider the kinetic properties of the drug–ADME–marker binding profiles, further refining the quality of compounds early in the drug discovery process. The promising results found in the literature indicate that kinetic data on drug–protein interactions may soon become a crucial decisive element in modern drug discovery.     

Nuclear magnetic resonance as a tool in drug discovery, metabolism and disposition

Nuclear magnetic resonance techniques have become critically important in the design of new pharmaceuticals, the characterization of drug-receptor interactions and metabolite identification. Advances in solvent suppression, coherent and incoherent magnetization transfer pathway selection, isotope editing and filtering, and diffusion filtering have made it possible to examine the interactions between small molecules and proteins or nucleic acids in great detail. Multiple schemes for high-throughput lead compound identification, metabolite screening and drug disposition have been proposed and reduced to practice. In particular, the coupling of NMR with other analytical methods, especially HPLC, combine the structural and dynamic detail available from NMR methods with the resolution and sensitivity of other analytical techniques.     

Analysing microarray data in drug discovery using systems biology

The innovation of present drug design focuses on new targets. However, compound efficacy and safety in human metabolism, including toxicity and pharmacokinetic profiles, but not target selection, are the criteria that determine which drug candidates enter the clinic. Systems biology approaches to disease are developed from the idea that disease-perturbed regulatory networks differ from their normal counterparts. Microarray data analyses reveal global changes in gene or protein expression in response to genetic and environmental changes and, accordingly, are well suited to construct the normal, disease-perturbed and drug-affected networks, which are useful for drug discovery in the pharmaceutical industry. The integration of modelling, microarray data and systems biology approaches will allow for a true breakthrough in in silico absorption, distribution, metabolism, excretion and toxicity assessment in drug design. Therefore, drug discovery through systems biology by means of microarray analyses could significantly reduce the time and cost of new drug development.     

A guide to drug discovery: Target selection in drug discovery

Target selection in drug discovery--defined here as the decision to focus on finding an agent with a particular biological action that is anticipated to have therapeutic utility--is influenced by a complex balance of scientific, medical and strategic considerations. In this article, we provide an introduction to the key issues in target selection and discuss the rationale for decision making.     

Cheminformatics: a tool for decision-makers in drug discovery

Cheminformatics is a tool that aims at facilitating the decision-making process across various preclinical stages of drug discovery. Access to biological and chemical data, but not the data themselves, is an integral part of cheminformatics. Emerging tools that allow storage of, and access to, chemical, structural-chemical and biological information are only now beginning to reach maturity. Recent advances in cheminformatics include virtual library analysis without enumeration and novel methods to investigate global chemical similarity and diversity voids. The most important task for cheminformatics is to constantly reevaluate itself and its utility in the area of drug discovery, in order to provide probabilistic, rather than categorical predictions.     

Diversity screening versus focussed screening in drug discovery

Which strategy is best for hit identification? Making the right choices in the capital-intensive world of modern drug discovery can make the difference between success and expensive failure. Keeping an open mind to all the options is essential. Two well-established strategies are diversity-based and focussed screening. This review will provide contrasting viewpoints highlighting the strengths and deficiencies of each approach, as well as some insights into why both strategies are likely to have a place in the research armoury of a successful drug company.     

Drosophila melanogaster as a model system for drug discovery and pathway screening

Although the freakish nightmare world of Mr Samsa may seem completely divorced from our view of the real world, the comparisons between the Drosophila and human genomes indicate that we are a lot closer to insects than we like to think. The use of sophisticated genetic approaches combined with emerging genomics technologies suggest that the fly has much to offer as a tool for understanding basic cellular processes and provides an attractive and complex model system for exploring the molecular basis of human diseases such as cancer, Alzheimer's disease and Huntington's disease.     

Protein NMR-based screening in drug discovery

Protein NMR as a screening tool in drug discovery has become prominent within the past ten years. Advances in protein manipulation, whether by biological means (labeling) or physical means (NMR), have created a powerful method that is able to observe ligand-target interactions in solution.     

Virtual screening in structure-based drug discovery

Recent advances in structure determination and computational methods have encouraged the development of structure-based virtual screening. Here we survey progress in the field and review the most recent methods, validation experiments and real applications, including an in-house example of hit identification for the oncology target Hsp90. These results provide a basis for discussing the current state of structure-based virtual screening and to outline the developments that are expected to have a major impact in the near future.