Data standards in drug discovery: A long way to go

Each year, millions to trillions of data points are generated to evaluate the response of chemicals and biologicals to human cells in vitro and in vivo using various technologies and endpoints. Despite the vast amount of data available, the development process has not become significantly more efficient in recent years. Given the increasing use of more complex physiological models, which are time-consuming and significantly more expensive, it is crucial to maximize the value of these valuable data through improved standardization.

Reducing False Positives through the Application of Fluorescence Lifetime Technology: A Comparative Study Using TYK2 Kinase as a Model System

The predominant assay detection methodologies used for enzyme inhibitor identification during early-stage drug discovery are fluorescence-based. Each fluorophore has a characteristic fluorescence decay, known as the fluorescence lifetime, that occurs throughout a nanosecond-to-millisecond timescale. The measurement of fluorescence lifetime as a reporter for biological activity is less common than fluorescence intensity, even though the latter has numerous issues that can lead to false-positive readouts. The confirmation of hit compounds as true inhibitors requires additional assays, cost, and time to progress from hit identification to lead drug-candidate optimization. To explore whether the use of fluorescence lifetime technology (FLT) can offer comparable benefits to label-free-based approaches such as RapidFire mass spectroscopy (RF-MS) and a superior readout compared to time-resolved fluorescence resonance energy transfer (TR-FRET), three equivalent assays were developed against the clinically validated tyrosine kinase 2 (TYK2) and screened against annotated compound sets. FLT provided a marked decrease in the number of false-positive hits when compared to TR-FRET. Further cellular screening confirmed that a number of potential inhibitors directly interacted with TYK2 and inhibited the downstream phosphorylation of the signal transducer and activator of transcription 4 protein (STAT4).

Selecting Approaches for Hit Identification and Increasing Options by Building the Efficient Discovery of Actionable Chemical Matter from DNA-Encoded Libraries

Over the past 20 years, the toolbox for discovering small-molecule therapeutic starting points has expanded considerably. Pharmaceutical researchers can now choose from technologies that, in addition to traditional high-throughput knowledge-based and diversity screening, now include the screening of fragment and fragment-like libraries, affinity selection mass spectrometry, and selection against DNA-encoded libraries (DELs). Each of these techniques has its own unique combination of advantages and limitations that makes them more, or less, suitable for different target classes or discovery objectives, such as desired mechanism of action. Layered on top of this are the constraints of the drug-hunters themselves, including budgets, timelines, and available platform capacity; each of these can play a part in dictating the hit identification strategy for a discovery program. In this article, we discuss some of the factors that we use to govern our building of a hit identification roadmap for a program and describe the increasing role that DELs are playing in our discovery strategy. Furthermore, we share our learning during our initial exploration of DEL and highlight the approaches we have evolved to maximize the value returned from DEL selections. Topics addressed include the optimization of library design and production, reagent validation, data analysis, and hit confirmation. We describe how our thinking in these areas has led us to build a DEL platform that has begun to deliver tractable matter to our global discovery portfolio.

A statistical framework for assessing pharmacological responses and biomarkers using uncertainty estimates

High-throughput testing of drugs across molecular-characterised cell lines can identify candidate treatments and discover biomarkers. However, the cells' response to a drug is typically quantified by a summary statistic from a best-fit dose-response curve, whilst neglecting the uncertainty of the curve fit and the potential variability in the raw readouts. Here, we model the experimental variance using Gaussian Processes, and subsequently, leverage uncertainty estimates to identify associated biomarkers with a new Bayesian framework. Applied to in vitro screening data on 265 compounds across 1074 cancer cell lines, our models identified 24 clinically established drug-response biomarkers, and provided evidence for six novel biomarkers by accounting for association with low uncertainty. We validated our uncertainty estimates with an additional drug screen of 26 drugs, 10 cell lines with 8 to 9 replicates. Our method is applicable to any dose-response data without replicates, and improves biomarker discovery for precision medicine.

DeepCOMO: from structure-activity relationship diagnostics to generative molecular design using the compound optimization monitor methodology

The compound optimization monitor (COMO) approach was originally developed as a diagnostic approach to aid in evaluating development stages of analog series and progress made during lead optimization. COMO uses virtual analog populations for the assessment of chemical saturation of analog series and has been further developed to bridge between optimization diagnostics and compound design. Herein, we discuss key methodological features of COMO in its scientific context and present a deep learning extension of COMO for generative molecular design, leading to the introduction of DeepCOMO. Applications on exemplary analog series are reported to illustrate the entire DeepCOMO repertoire, ranging from chemical saturation and structure-activity relationship progression diagnostics to the evaluation of different analog design strategies and prioritization of virtual candidates for optimization efforts, taking into account the development stage of individual analog series.

A painful lesson from the COVID-19 pandemic: the need for broad-spectrum, host-directed antivirals

While the COVID-19 pandemic has spurred intense research and collaborative discovery worldwide, the development of a safe, effective, and targeted antiviral from the ground up is time intensive. Therefore, most antiviral discovery efforts are focused on the re-purposing of clinical stage or approved drugs. While emerging data on drugs undergoing COVID-19 repurpose are intriguing, there is an undeniable need to develop broad-spectrum antivirals to prevent future viral pandemics of unknown origin. The ideal drug to curtail rapid viral spread would be a broad-acting agent with activity against a wide range of viruses. Such a drug would work by modulating host-proteins that are often shared by multiple virus families thereby enabling preemptive drug development and therefore rapid deployment at the onset of an outbreak. Targeting host-pathways and cellular proteins that are hijacked by viruses can potentially offer broad-spectrum targets for the development of future antiviral drugs. Such host-directed antivirals are also likely to offer a higher barrier to the development and selection of drug resistant mutations. Given that most approved antivirals do not target host-proteins, we reinforce the need for the development of such antivirals that can be used in pre- and post-exposure populations.

A practical guide to secondary pharmacology in drug discovery

Secondary pharmacological profiling is increasingly applied in pharmaceutical drug discovery to address unwanted pharmacological side effects of drug candidates before entering the clinic. Regulators, drug makers and patients share a demand for deep characterization of secondary pharmacology effects of novel drugs and their metabolites. The scope of such profiling has therefore expanded substantially in the past two decades, leading to the implementation of broad in silico profiling methods and focused in vitro off-target screening panels, to identify liabilities, but also opportunities, as early as possible. The pharmaceutical industry applies such panels at all stages of drug discovery routinely up to early development. Nevertheless, target composition, screening technologies, assay formats, interpretation and scheduling of panels can vary significantly between companies in the absence of dedicated guidelines. To contribute towards best practices in secondary pharmacology profiling, this review aims to summarize the state-of-the art in this field. Considerations are discussed with respect to panel design, screening strategy, implementation and interpretation of the data, including regulatory perspectives. The cascaded, or integrated, use of in silico and off-target profiling allows to exploit synergies for comprehensive safety assessment of drug candidates.

An open-source drug discovery platform enables ultra-large virtual screens

On average, an approved drug currently costs US$2-3 billion and takes more than 10 years to develop1. In part, this is due to expensive and time-consuming wet-laboratory experiments, poor initial hit compounds and the high attrition rates in the (pre-)clinical phases. Structure-based virtual screening has the potential to mitigate these problems. With structure-based virtual screening, the quality of the hits improves with the number of compounds screened2. However, despite the fact that large databases of compounds exist, the ability to carry out large-scale structure-based virtual screening on computer clusters in an accessible, efficient and flexible manner has remained difficult. Here we describe VirtualFlow, a highly automated and versatile open-source platform with perfect scaling behaviour that is able to prepare and efficiently screen ultra-large libraries of compounds. VirtualFlow is able to use a variety of the most powerful docking programs. Using VirtualFlow, we prepared one of the largest and freely available ready-to-dock ligand libraries, with more than 1.4 billion commercially available molecules. To demonstrate the power of VirtualFlow, we screened more than 1 billion compounds and identified a set of structurally diverse molecules that bind to KEAP1 with submicromolar affinity. One of the lead inhibitors (iKeap1) engages KEAP1 with nanomolar affinity (dissociation constant (Kd) = 114 nM) and disrupts the interaction between KEAP1 and the transcription factor NRF2. This illustrates the potential of VirtualFlow to access vast regions of the chemical space and identify molecules that bind with high affinity to target proteins.

Validating the validation: reanalyzing a large-scale comparison of deep learning and machine learning models for bioactivity prediction

Machine learning methods may have the potential to significantly accelerate drug discovery. However, the increasing rate of new methodological approaches being published in the literature raises the fundamental question of how models should be benchmarked and validated. We reanalyze the data generated by a recently published large-scale comparison of machine learning models for bioactivity prediction and arrive at a somewhat different conclusion. We show that the performance of support vector machines is competitive with that of deep learning methods. Additionally, using a series of numerical experiments, we question the relevance of area under the receiver operating characteristic curve as a metric in virtual screening. We further suggest that area under the precision–recall curve should be used in conjunction with the receiver operating characteristic curve. Our numerical experiments also highlight challenges in estimating the uncertainty in model performance via scaffold-split nested cross validation.

PathFX provides mechanistic insights into drug efficacy and safety for regulatory review and therapeutic development

Failure to demonstrate efficacy and safety issues are important reasons that drugs do not reach the market. An incomplete understanding of how drugs exert their effects hinders regulatory and pharmaceutical industry projections of a drug's benefits and risks. Signaling pathways mediate drug response and while many signaling molecules have been characterized for their contribution to disease or their role in drug side effects, our knowledge of these pathways is incomplete. To better understand all signaling molecules involved in drug response and the phenotype associations of these molecules, we created a novel method, PathFX, a non-commercial entity, to identify these pathways and drug-related phenotypes. We benchmarked PathFX by identifying drugs' marketed disease indications and reported a sensitivity of 41%, a 2.7-fold improvement over similar approaches. We then used PathFX to strengthen signals for drug-adverse event pairs occurring in the FDA Adverse Event Reporting System (FAERS) and also identified opportunities for drug repurposing for new diseases based on interaction paths that associated a marketed drug to that disease. By discovering molecular interaction pathways, PathFX improved our understanding of drug associations to safety and efficacy phenotypes. The algorithm may provide a new means to improve regulatory and therapeutic development decisions.

Discovery and Structure-Based Optimization of Benzimidazole-Derived Activators of SOS1-Mediated Nucleotide Exchange on RAS

Son of sevenless homologue 1 (SOS1) is a guanine nucleotide exchange factor that catalyzes the exchange of GDP for GTP on RAS. In its active form, GTP-bound RAS is responsible for numerous critical cellular processes. Aberrant RAS activity is involved in ∼30% of all human cancers; hence, SOS1 is an attractive therapeutic target for its role in modulating RAS activation. Here, we describe a new series of benzimidazole-derived SOS1 agonists. Using structure-guided design, we discovered small molecules that increase nucleotide exchange on RAS in vitro at submicromolar concentrations, bind to SOS1 with low double-digit nanomolar affinity, rapidly enhance cellular RAS-GTP levels, and invoke biphasic signaling changes in phosphorylation of ERK 1/2. These compounds represent the most potent series of SOS1 agonists reported to date.

Integration of Lead Discovery Tactics and the Evolution of the Lead Discovery Toolbox

There has been much debate around the success rates of various screening strategies to identify starting points for drug discovery. Although high-throughput target-based and phenotypic screening has been the focus of this debate, techniques such as fragment screening, virtual screening, and DNA-encoded library screening are also increasingly reported as a source of new chemical equity. Here, we provide examples in which integration of more than one screening approach has improved the campaign outcome and discuss how strengths and weaknesses of various methods can be used to build a complementary toolbox of approaches, giving researchers the greatest probability of successfully identifying leads. Among others, we highlight case studies for receptor-interacting serine/threonine-protein kinase 1 and the bromo- and extra-terminal domain family of bromodomains. In each example, the unique insight or chemistries individual approaches provided are described, emphasizing the synergy of information obtained from the various tactics employed and the particular question each tactic was employed to answer. We conclude with a short prospective discussing how screening strategies are evolving, what this screening toolbox might look like in the future, how to maximize success through integration of multiple tactics, and scenarios that drive selection of one combination of tactics over another.

Preclinical QSP Modeling in the Pharmaceutical Industry: An IQ Consortium Survey Examining the Current Landscape

A cross-industry survey was conducted to assess the landscape of preclinical quantitative systems pharmacology (QSP) modeling within pharmaceutical companies. This article presents the survey results, which provide insights on the current state of preclinical QSP modeling in addition to future opportunities. Our results call attention to the need for an aligned definition and consistent terminology around QSP, yet highlight the broad applicability and benefits preclinical QSP modeling is currently delivering.

Setbacks in Alzheimer research demand new strategies, not surrender

In this month's editorial, the PLOS Medicine Editors discuss the challenges of addressing a growing population with Alzheimer disease and dementia amidst disappointing news from the pharmaceutical industry.

Accelerating Lead Compound Optimization

Chemists at The Scripps Research Institute in La Jolla, CA, and Pfizer's La Jolla Laboratories have devised a new way to rapidly synthesize strained-ring structures, which are increasingly favored to optimize potential drugs. With this method, strain-release amination, Pfizer researchers were able to produce sufficient quantities of a particular structure they needed to evaluate a promising cancer drug candidate.

Integration of Regulatory Guidelines into Protein Drug Product Development

The drug product development process for proteins went through its infancy in the early eighties of last century and is in its maturity today. This has been driven largely by the rapid growth of the biotechnology industry, which led to the development and issuance of many regulatory guidelines/directories, especially those through the International Conference of Harmonization (ICH). These guidelines have certainly guided different aspects of a drug product development process. On the other hand, they were issued separately on different topics and in different time periods. An integration of all relevant guidelines into the corresponding areas in drug product development would greatly facilitate the development process. The purpose of this short review is to integrate the relevant (mainly ICH) regulatory guidelines into protein drug product development and to discuss remaining issues, which may lead to further revision of existing guidelines or development of new ones. Drug product development scientists need to collect adequate and relevant development data for a successful product registration. The key is the ability to justify the final drug product in terms of choice of the drug product formulation, container closure system, and manufacturing process.

LAY ABSTRACT

The drug product development process for proteins has matured today, largely due to the rapid growth of the biotechnology industry. In this process, many regulatory guidelines/directories were developed and issued, especially through the International Conference of Harmonization (ICH). However, they were issued separately on different topics and in different time periods. An integration of all relevant guidelines into the corresponding areas in drug product development would greatly facilitate the development process. The purpose of this short review is to integrate the relevant (mainly ICH) regulatory guidelines into protein drug product development and to discuss remaining issues, which may lead to further revision of existing guidelines or development of new ones. Drug product development scientists need to collect adequate and relevant development data for a successful product registration. The key is the ability to justify the final drug product in terms of choice of the product formulation, container closure system, and manufacturing process.

Missed Opportunities in the Patient-Focused Drug Development Public Meeting and Scientific Workshop on Female Sexual Dysfunction Held at the FDA, October 2014

There were numerous missed opportunities at the October 2014 U.S. Food and Drug Administration (FDA) meeting on female sexual dysfunction (FSD). They included opportunities to hear from a diverse range of patients and to engage in evidence-based discussions of unmet medical needs, diagnostic instruments, trial end points, and inclusion criteria for clinical trials. Contributions of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) nomenclature, based on extensive research, were dismissed in favor of language favoring a seemingly clear but scientifically unsupportable distinction between women's sexual desire and arousal. Numerous participants, including patients recruited by their physicians, acknowledged travel expenses paid for by interested pharmaceutical companies. Conflicts of interest were manifold. The meeting did not advance the FDA's understanding of women's sexual distress and represents a setback for our field.

Mitigating risk in academic preclinical drug discovery

The number of academic drug discovery centres has grown considerably in recent years, providing new opportunities to couple the curiosity-driven research culture in academia with rigorous preclinical drug discovery practices used in industry. To fully realize the potential of these opportunities, it is important that academic researchers understand the risks inherent in preclinical drug discovery, and that translational research programmes are effectively organized and supported at an institutional level. In this article, we discuss strategies to mitigate risks in several key aspects of preclinical drug discovery at academic drug discovery centres, including organization, target selection, assay design, medicinal chemistry and preclinical pharmacology.

TCM-based new drug discovery and development in China

Over the past 30 years, China has significantly improved the drug development environment by establishing a series of policies for the regulation of new drug approval. The regulatory system for new drug evaluation and registration in China was gradually developed in accordance with international standards. The approval and registration of TCM in China became as strict as those of chemical drugs and biological products. In this review, TCM-based new drug discovery and development are introduced according to the TCM classification of nine categories.

The immune space: a concept and template for rationalizing vaccine development

Empirical testing of candidate vaccines has led to the successful development of a number of lifesaving vaccines. The advent of new tools to manipulate antigens and new methods and vectors for vaccine delivery has led to a veritable explosion of potential vaccine designs. As a result, selection of candidate vaccines suitable for large-scale efficacy testing has become more challenging. This is especially true for diseases such as dengue, HIV, and tuberculosis where there is no validated animal model or correlate of immune protection. Establishing guidelines for the selection of vaccine candidates for advanced testing has become a necessity. A number of factors could be considered in making these decisions, including, for example, safety in animal and human studies, immune profile, protection in animal studies, production processes with product quality and stability, availability of resources, and estimated cost of goods. The "immune space template" proposed here provides a standardized approach by which the quality, level, and durability of immune responses elicited in early human trials by a candidate vaccine can be described. The immune response profile will demonstrate if and how the candidate is unique relative to other candidates, especially those that have preceded it into efficacy testing and, thus, what new information concerning potential immune correlates could be learned from an efficacy trial. A thorough characterization of immune responses should also provide insight into a developer's rationale for the vaccine's proposed mechanism of action. HIV vaccine researchers plan to include this general approach in up-selecting candidates for the next large efficacy trial. This "immune space" approach may also be applicable to other vaccine development endeavors where correlates of vaccine-induced immune protection remain unknown.

Orthogonal test design for optimization of the extraction of flavonid from the Fructus Gardeniae

OBJECTIVE

It is imperative to provide some consistent experimental results for the extraction of flavonid from Fructus Gardeniae.

METHODS

The key extraction parameters that influenced the yield of flavonid from Fructus Gardeniae were optimized by employing an orthogonal experiment [L(9)(3)(4)], including the ratio of buffer solution (Na(2)B(4)O(7)·10H(2)O) to raw material, concentration of Fructus Gardeniae in extracting solution, extraction time and pH of buffer solution. An UV/Vis detector was used to perform the qualitative and quantitative analyses of the extracted flavonid with the using of the standard sample.

RESULTS

The maximum extraction yield of the crude extract was 5.0533 (mg/g) after 20 min when the mass ratio of Na(2)B(4)O(7)·10H(2)O to raw material was 0.4%, the concentration of Fructus Gardeniae in the extraction solution was 1/12 (g/mL), and pH of buffer solution was 4.5. The positive reactions to the Molish and HCl-Mg tests suggested that the extracted compound was flavonoid, and FTIR measurements also identified the presence of flavonoid in the extracts.

CONCLUSION

This work is expected to provide a basis for further research, development, and utilization of Fructus gardenia in flavonid extraction.

It is time for a paradigm shift in drug discovery bioanalysis: from SRM to HRMS

It can be argued that the last true paradigm shift in the bioanalytical (BA) arena was the shift from high-performance liquid chromatography (HPLC) with ultraviolet (UV) detection to HPLC with tandem mass spectrometry (MS/MS) detection after the commercialization of the triple quadrupole mass spectrometer in the 1990s. HPLC-MS/MS analysis based on selected reaction monitoring (SRM) has become the gold standard for BA assays and is used by all the major pharmaceutical companies for the quantitative analysis of new drug entities (NCEs) as part of the new drug discovery and development process. While LC-MS/MS continues to be the best tool for drug discovery bioanalysis, a new paradigm involving high-resolution mass spectrometry (HRMS) and ultrahigh-pressure liquid chromatography (uHPLC) is starting to make inroads into the pharmaceutical industry. The ability to collect full scan spectra, with excellent mass accuracy, mass resolution, 10-250 ms scan speeds and no NCE-related MS parameter optimization, makes the uHPLC-HRMS techniques suitable for quantitative analysis of NCEs while preserving maximum qualitative information about other drug-related and endogenous components such as metabolites, degradants, biomarkers and formulation materials. In this perspective article, we provide some insight into the evolution of the hybrid quadrupole-time-of-flight (Qq-TOF) mass spectrometer and propose some of the desirable specifications that such HRMS systems should have to be integrated into the drug discovery bioanalytical workflow for performing integrated qualitative and quantitative bioanalysis of drugs and related components.

Ultrafast mass spectrometry based bioanalytical method for digoxin supporting an in vitro P-glycoprotein (P-gp) inhibition screen

The evaluation of interactions between drug candidates and transporters such as P-glycoprotein (P-gp) has gained considerable interest in drug discovery and development. Inhibition of P-gp can be assessed by performing bi-directional permeability studies with in vitro P-gp-expressing cellular model systems such as Caco-2 (human colon carcinoma) cells, using digoxin as a substrate probe. Existing methodologies include either assaying (3)H-digoxin with liquid scintillation counting (LSC) detection or assaying non-labeled digoxin with liquid chromatography-tandem mass spectrometric (LC-MS/MS) analysis at a speed of several minutes per sample. However, it is not feasible to achieve a throughput high enough using these approaches to sustain an early liability screen that generates more than a thousand samples on a daily basis. To address this challenge, we developed an ultrafast (9 s per sample) bioanalytical method for digoxin analysis using RapidFire™, an on-line solid-phase extraction (SPE) system, with MS/MS detection. A stable isotope labeled analog, d3-digoxin, was used as internal standard to minimize potential ionization matrix effect during the RF-MS/MS analysis. The RF-MS/MS method was more than 16 times faster than the LC-MS/MS method but demonstrated similar sensitivity, selectivity, reproducibility, linearity and robustness. P-gp inhibition results of multiple validation compounds obtained with this RF-MS/MS method were in agreement with those generated by both the LC-MS/MS method and the (3)H-radiolabel assay. This method has been successfully deployed to assess P-gp inhibition potential as an important early liability screen for drug-transporter interaction.

Drug discovery: lessons from evolution

A common view within the pharmaceutical industry is that there is a problem with drug discovery and we should do something about it. There is much sympathy for this from academics, regulators and politicians. In this article I propose that lessons learnt from evolution help identify those factors that favour successful drug discovery. This personal view is influenced by a decade spent reviewing drug development programmes submitted for European regulatory approval. During the prolonged gestation of a new medicine few candidate molecules survive. This process of elimination of many variants and the survival of so few has much in common with evolution, an analogy that encourages discussion of the forces that favour, and those that hinder, successful drug discovery. Imagining a world without vaccines, anaesthetics, contraception and anti-infectives reveals how medicines revolutionized humanity. How to manipulate conditions that favour such discoveries is worth consideration.

Running a tightrope: regulatory challenges in the development of antiretrovirals

Since the approval of Retrovir, (zidovudine, AZT) in 1987 by the Food and Drug Administration, a number of regulatory initiatives were codified into regulation which contributed to the rapid development of new treatments for HIV-1 infection. These initiatives are a testament to the efforts of AIDS activists and regulators to improve access to drugs for serious and life-threatening diseases. Currently, 28 antiretroviral drugs and combinations of antiretrovirals are available to treat HIV-1 infection. The broadening armamentarium of approved antiretroviral drugs provides new options and more choices for physicians and HIV patients. Importantly, the introduction of these newly approved HIV drugs has shown that the majority of HIV-1-infected treatment-naïve and treatment-experienced patients can achieve maximal virologic suppression (less than 50 copies/mL HIV-1 RNA). This article describes the past and current regulatory challenges in the development of new HIV treatments and provides an overview of the drug regulations that were required for the approval of HIV drugs. This article forms part of a special issue of Antiviral Research marking the 25th anniversary of antiretroviral drug discovery and development, Vol 85, issue 1, 2010.

A crowdsourcing evaluation of the NIH chemical probes

Between 2004 and 2008, the US National Institutes of Health Molecular Libraries and Imaging initiative pilot phase funded 10 high-throughput screening centers, resulting in the deposition of 691 assays into PubChem and the nomination of 64 chemical probes. We crowdsourced the Molecular Libraries and Imaging initiative output to 11 experts, who expressed medium or high levels of confidence in 48 of these 64 probes.

["Non-aging" pearl oyster and aging salmon (about lack of basis for production of medicines based on European pearl oyster Margaritifera margaritifera)]

The article focuses on disproof of the claim by Valery Ziuganov, that pearl oyster infection prolongs the lifespan of salmon and that pearl oyster can be used as a source of medicine. His activity on the production of medicine pretending to care cancer and senescence based on the gills of salmon infected by pearl oyster is blamed. The data on pearl oyster and salmon biology are presented: Atlantic salmon survives after spawning irrespectively of oyster infection. The problem of shady medicines production is discussed. It was shown that even Academic editions and formal attributes of the belonging to scientific community do not provide a necessary barrier to frauds in this field. Adaptationist program popularity contributes such a situation, because it provides "grounds" for any speculation concerning advantages of any biological process.

Stimulating research and development of pharmaceutical products for neglected diseases

This article provides an introduction to the problem of pharmaceutical research and development of medicinal products for neglected diseases in developing countries. The prevailing market-based intellectual property incentive model of global pharmaceutical innovation has focussed health research priorities mainly on needs of industrialized countries. Increased international attention to the lack of health tools to prevent or treat diseases affecting impoverished regions has raised the discussion on how to encourage sustainable innovation in this area with little or no market. Proposals include models such as for example prize funds, advanced purchase commitments patent pools or creative licensing strategies and seek to complement the current intellectual property based incentive mechanism and government's innovation policy instruments such as tax credits or public research funds. Indispensable requirement to tackle the issue of neglected diseases, however, is a concerted commitment of all stakeholders on national and international levels.