Innovation in the pharmaceutical industry

Is Target-Based Drug Discovery Efficient? Discovery and "Off-Target" Mechanisms of All Drugs

Target-based drug discovery is the dominant paradigm of drug discovery; however, a comprehensive evaluation of its real-world efficiency is lacking. Here, a manual systematic review of about 32000 articles and patents dating back to 150 years ago demonstrates its apparent inefficiency. Analyzing the origins of all approved drugs reveals that, despite several decades of dominance, only 9.4% of small-molecule drugs have been discovered through "target-based" assays. Moreover, the therapeutic effects of even this minimal share cannot be solely attributed and reduced to their purported targets, as they depend on numerous off-target mechanisms unconsciously incorporated by phenotypic observations. The data suggest that reductionist target-based drug discovery may be a cause of the productivity crisis in drug discovery. An evidence-based approach to enhance efficiency seems to be prioritizing, in selecting and optimizing molecules, higher-level phenotypic observations that are closer to the sought-after therapeutic effects using tools like artificial intelligence and machine learning.

On drug discovery against infectious diseases and academic medicinal chemistry contributions

This perspective is an attempt to document the problems that medicinal chemists are facing in drug discovery. It is also trying to identify relevant/possible, research areas in which academics can have an impact and should thus be the subject of grant calls. Accordingly, it describes how hit discovery happens, how compounds to be screened are selected from available chemicals and the possible reasons for the recurrent paucity of useful/exploitable results reported. This is followed by the successful hit to lead stories leading to recent and original antibacterials which are, or about to be, used in human medicine. Then, illustrated considerations and suggestions are made on the possible inputs of academic medicinal chemists. This starts with the observation that discovering a "good" hit in the course of a screening campaign still rely on a lot of luck - which is within the reach of academics -, that the hit to lead process requires a lot of chemistry and that if public-private partnerships can be important throughout these stages, they are absolute requirements for clinical trials. Concerning suggestions to improve the current hit success rate, one academic input in organic chemistry would be to identify new and pertinent chemical space, design synthetic accesses to reach these and prepare the corresponding chemical libraries. Concerning hit to lead programs on a given target, if no new hits are available, previously reported leads along with new structural data can be pertinent starting points to design, prepare and assay original analogues. In conclusion, this text is an actual plea illustrating that, in many countries, academic research in medicinal chemistry should be more funded, especially in the therapeutic area neglected by the industry. At the least, such funds would provide the intensive to secure series of hopefully relevant chemical entities which appears to often lack when considering the results of academic as well as industrial screening campaigns.

Determinants of radical drug innovation: a systematic literature review

Radical drug innovations are of great importance to pharmaceutical firms and public health. Understanding the determinants involved in successful radical drug innovations is key to increasing this type of output in the future. The objective of this review is to search the literature for key firm-level determinants of radical drug innovation. Following a systematic literature review approach, we considered more than 4100 peer-reviewed journal articles and PhD theses, of which we included 38 in the narrative synthesis. To guide the review, we use Crossan and Apaydin’s (J Manag Stud 47:1154–1191, 2010) model of firm-level determinants of innovation for the first time within the pharmaceutical industry, which is unique due to the risks, costs, and time frames associated with radical drug innovation. We focus on three groups of determinants: leadership, managerial levers, and business processes. We find the following to be particularly important for radical drug innovation: external knowledge sourcing (managerial lever); internal knowledge management (managerial lever); ability of top leaders to innovate, as determined by educational background and professional experience (leadership); and leaders’ focus on shaping innovation and performance cultures (leadership). We offer a conceptual framework of critical determinants of radical drug innovation and highlight managerial implications. We also discuss gaps in radical drug innovation research and provide suggestions for future study. Many of the findings discussed in this paper are contradictory because they rely on different definitions and measures, which inhibits our full and accurate understanding of radical drug innovation development. More research is needed to address untested measures of radical drug innovation.

Prediction of Side Effects Using Comprehensive Similarity Measures

Identifying the potential side effects of drugs is crucial in clinical trials in the pharmaceutical industry. The existing side effect prediction methods mainly focus on the chemical and biological properties of drugs. This study proposes a method that uses diverse information such as drug-drug interactions from DrugBank, drug-drug interactions from network, single nucleotide polymorphisms, and side effect anatomical hierarchy as well as chemical structures, indications, and targets. The proposed method is based on the assumption that properties used in drug repositioning studies could be utilized to predict side effects because the phenotypic expression of a side effect is similar to that of the disease. The prediction results using the proposed method showed a 3.5% improvement in the area under the curve (AUC) over that obtained when only chemical, indication, and target features were used. The random forest model delivered outstanding results for all combinations of feature types. Finally, after identifying candidate side effects of drugs using the proposed method, the following four popular drugs were discussed: (1) dasatinib, (2) sitagliptin, (3) vorinostat, and (4) clonidine.

From Basic Research to Competitiveness: An Econometric Analysis of the Global Pharmaceutical Sector

The pharmaceutical sector is a flagship of the economy in most developed countries and is one of the most research-intensive sectors of modern economies. The aim of this paper was to determine the mutual relationships between the research and development (R&D) resources, various indicators of scientific performance and the competitiveness of the sector. We carried out a cointegration analysis of a time series of R+D resources, the number of academic papers published, as well as patents and the competitiveness of this sector in various developed states. The econometric analysis of time series is built on panel cointegration models. Based on the combination of different comprehensive and coherent international databases and applying the latest methods of modern time series analysis, the paper proves that, in most developed countries, a direct, significant, causal, lagged relationship between the monetary resources allocated to R&D and the number of academic papers, as well as the number of patents can be observed. In most cases, a causal relationship can be demonstrated between the number of academic papers and patents, but vice versa, this fact is not provable. This study highlights the complexity of innovation systems in the pharmaceutical sector. The results prove only a weak connection between the number of patents and the number of publications. When evaluating the efficiency of the utilisation of resources allocated to pharmaceutical R+D, the effect of the time lag should be considered.

Does myo-inositol effect on PCOS follicles involve cytoskeleton regulation?

Inositol metabolism is severely impaired in follicles obtained from cystic ovaries, leading to deregulated insulin transduction and steroid synthesis. On the contrary, inositol administration to women suffering from polycystic ovary syndrome (PCOS) has been proven to efficiently counteract most of the clinical hallmarks displayed by PCOS patients, including insulin resistance, hyperandrogenism and oligo-amenorrhea. We have recently observed that myo-inositol induces significant changes in cytoskeletal architecture of breast cancer cells, by modulating different biochemical pathways, eventually modulating the epithelial-mesenchymal transition. We hypothesize that inositol and its monophosphate derivatives, besides their effects on insulin transduction, may efficiently revert histological and functional features of cystic ovary by inducing cytoskeleton rearrangements. We propose an experimental model that could address not only whether inositol modulates cytoskeleton dynamics in both normal and cystic ovary cells, but also whether this effect may interfere with ovarian steroidogenesis. A more compelling understanding of the mechanisms of action of inositol (and its derivatives) would greatly improve its therapeutic utilization, by conferring to current treatments a well-grounded scientific rationale.

When Quality Beats Quantity: Decision Theory, Drug Discovery, and the Reproducibility Crisis

A striking contrast runs through the last 60 years of biopharmaceutical discovery, research, and development. Huge scientific and technological gains should have increased the quality of academic science and raised industrial R&D efficiency. However, academia faces a "reproducibility crisis"; inflation-adjusted industrial R&D costs per novel drug increased nearly 100 fold between 1950 and 2010; and drugs are more likely to fail in clinical development today than in the 1970s. The contrast is explicable only if powerful headwinds reversed the gains and/or if many "gains" have proved illusory. However, discussions of reproducibility and R&D productivity rarely address this point explicitly. The main objectives of the primary research in this paper are: (a) to provide quantitatively and historically plausible explanations of the contrast; and (b) identify factors to which R&D efficiency is sensitive. We present a quantitative decision-theoretic model of the R&D process. The model represents therapeutic candidates (e.g., putative drug targets, molecules in a screening library, etc.) within a "measurement space", with candidates' positions determined by their performance on a variety of assays (e.g., binding affinity, toxicity, in vivo efficacy, etc.) whose results correlate to a greater or lesser degree. We apply decision rules to segment the space, and assess the probability of correct R&D decisions. We find that when searching for rare positives (e.g., candidates that will successfully complete clinical development), changes in the predictive validity of screening and disease models that many people working in drug discovery would regard as small and/or unknowable (i.e., an 0.1 absolute change in correlation coefficient between model output and clinical outcomes in man) can offset large (e.g., 10 fold, even 100 fold) changes in models' brute-force efficiency. We also show how validity and reproducibility correlate across a population of simulated screening and disease models. We hypothesize that screening and disease models with high predictive validity are more likely to yield good answers and good treatments, so tend to render themselves and their diseases academically and commercially redundant. Perhaps there has also been too much enthusiasm for reductionist molecular models which have insufficient predictive validity. Thus we hypothesize that the average predictive validity of the stock of academically and industrially "interesting" screening and disease models has declined over time, with even small falls able to offset large gains in scientific knowledge and brute-force efficiency. The rate of creation of valid screening and disease models may be the major constraint on R&D productivity.

Crowdsourced 'R&D' and medical research

INTRODUCTION OR BACKGROUND

Crowdsourced R&D, a research methodology increasingly applied to medical research, has properties well suited to large-scale medical data collection and analysis, as well as enabling rapid research responses to crises such as disease outbreaks.

SOURCES OF DATA

Multidisciplinary literature offers diverse perspectives of crowdsourced R&D as a useful large-scale medical data collection and research problem-solving methodology.

AREAS OF AGREEMENT

Crowdsourced R&D has demonstrated 'proof of concept' in a host of different biomedical research applications.

AREAS OF CONTROVERSY

A wide range of quality and ethical issues relate to crowdsourced R&D.

GROWING POINTS

The rapid growth in applications of crowdsourced R&D in medical research is predicted by an increasing body of multidisciplinary theory.

AREAS FOR TIMELY RESEARCH

Further research in areas such as artificial intelligence may allow better coordination and management of the high volumes of medical data and problem-solving inputs generated by the crowdsourced R&D process.

Post-approval fate of pharmaceutical companies

For a fortunate subset of pharmaceutical companies, a regulatory approval is the culmination of massive investment in time, work and money. What happens next? Some companies proceed to build a pipeline and obtain additional approvals. Others do not. In this present report, post-approval fate is evaluated and it was found that most companies are ultimately acquired. A subset achieved a second approval six-to-eight years after the first approval, whereas a shrinking subset, designated as 'singlets', remains active in drug discovery with only a single approval. The likelihood that a company will remain a singlet or be acquired relates to therapeutic indication, with oncology associated with increased acquisition potential and infectious-disease-based companies being less commonly acquired.

Fever in Cancer Treatment: Coley's Therapy and Epidemiologic Observations

In the fall of 1890, an athletic, self-possessed, and thoughtful 17-year-old girl, who had just returned from an adventurous trip to Alaska where she had hurt her hand in a trivial accident, went to see a young, innovative surgeon in his new practice in New York City. Barely out of Harvard Medical School, he was a rising star in New York surgical circles, and the young woman asked him for help with her poorly healing, swollen, and naggingly painful injury. This visit had a far-reaching effect on cancer research, American philanthropy, and the career of the young man, William Coley, MD (1862-1936, Figure 1). The patient, Elisabeth Dashiell, confidant and close friend of John D. Rockefeller, Jr, was diagnosed by Coley with a highly aggressive round cell sarcoma, and despite radical surgery and in spite of Coley's undoubtedly fine surgical skills and intensive care, a rapid progression of the cancer, immense suffering, and Elisabeth's death a few months later could not be prevented.

Modeling natural anti-inflammatory compounds by molecular topology

One of the main pharmacological problems today in the treatment of chronic inflammation diseases consists of the fact that anti-inflammatory drugs usually exhibit side effects. The natural products offer a great hope in the identification of bioactive lead compounds and their development into drugs for treating inflammatory diseases. Computer-aided drug design has proved to be a very useful tool for discovering new drugs and, specifically, Molecular Topology has become a good technique for such a goal. A topological-mathematical model, obtained by linear discriminant analysis, has been developed for the search of new anti-inflammatory natural compounds. An external validation obtained with the remaining compounds (those not used in building up the model), has been carried out. Finally, a virtual screening on natural products was performed and 74 compounds showed actual anti-inflammatory activity. From them, 54 had been previously described as anti-inflammatory in the literature. This can be seen as a plus in the model validation and as a reinforcement of the role of Molecular Topology as an efficient tool for the discovery of new anti-inflammatory natural compounds.

Biobank research and ethics: the problem of informed consent in Polish biobanks

INTRODUCTION

The dynamic development of biobanks causes some ethical, social, and legal problems. The most discussed problems are obtaining informed consent, especially for future research, from minors and from deceased people. The aim of this article is to present the current standards held by Polish biobanks concerning obtaining a participant's informed consent in some aspects.

MATERIAL AND METHODS

Survey was carried out by anonymous questionnaire among 59 institutions which deal with the collecting and storage of human cells and tissues in the year 2008. Twenty four filled-in copies of the questionnaires were sent back (return=41%).

RESULTS

Almost every institution (92%) obtains written consent, but a third of the surveyed institutions (29%) do not obtain consent for the future use of the samples. The majority of the respondents (83%) support the idea of using biological materials for research purposes of a donor who died if he did not leave any written objection to such practices and 46% of respondents stated that biobanks should obtain the consent from the already mature donor who gave their samples as a child.

CONCLUSIONS

The practice and rules for obtaining informed consent for the scientific research require improvement. The possibility to use the human materials in the future, conditions for getting access to the data, the possibility of their withdrawal from the database and using the materials and data after the death of the donor should be clearly determined when the informed consent to collect the material is obtained.

Clinical judgement and the medical profession

OBJECTIVES

Clinical judgment is a central element of the medical profession, essential for the performance of the doctor, and potentially generating information also for other clinicians and for scientists and health care managers. The recently renewed interest in clinical judgement is primarily engaged with its role in communication, diagnosis and decision making. Beyond this issue, the present article highlights the interrelations between clinical judgement, therapy assessment and medical professionalism.

METHODS

Literature review and theory development.

RESULTS

The article presents different methodological approaches to causality assessment in clinical studies and in clinical judgement, and offers criteria for clinical single case causality. The article outlines models of medical professionalism such as technical rationality and practice epistemology, and characterizes features of professional expertise such as tacit knowledge, reflection in action, and gestalt cognition.

CONCLUSIONS

Consequences of a methodological and logistical advancement of clinical judgment are discussed, both in regard to medical progress and to the renewal of the cognitive basis of the medical profession.

Topological virtual screening: a way to find new compounds active in ulcerative colitis by inhibiting NF-κB

Ulcerative colitis and Crohn's disease are chronic, immune-mediated inflammatory diseases of the gastrointestinal tract. Nuclear Factor Kappa B (NF-κB) is a transcription factor that plays a key role in regulating expression of multiple inflammatory and immune genes. In this study, a Topological Virtual Screening study has been carried out to achieve a model capable of finding new compounds active in ulcerative colitis by inhibiting NF-κB. Different topological indices were used as structural descriptors, and their relation to biological activity was determined using linear discriminant analysis. A topological model consisting of two discriminant functions was built up. The first function focused in the discrimination between NF-κB active and inactive compounds, and the second one in distinguishing between compounds active and inactive on ulcerative colitis. The model was then applied sequentially to a large database of compounds with unknown activity. Twenty-eight of such compounds were predicted to be active and selected for in vitro and in vivo testing.

Bond-based 2D quadratic fingerprints in QSAR studies: virtual and in vitro tyrosinase inhibitory activity elucidation

In this report, we show the results of quantitative structure-activity relationship (QSAR) studies of tyrosinase inhibitory activity, by using the bond-based quadratic indices as molecular descriptors (MDs) and linear discriminant analysis (LDA), to generate discriminant functions to predict the anti-tyrosinase activity. The best two models [Eqs (6) and (12)] out of the total 12 QSAR models developed here show accuracies of 93.51% and 91.21%, as well as high Matthews correlation coefficients (C) of 0.86 and 0.82, respectively, in the training set. The validation external series depicts values of 90.00% and 89.44% for these best two equations (6) and (12), respectively. Afterwards, a second external prediction data are used to perform a virtual screening of compounds reported in the literature as active (tyrosinase inhibitors). In a final step, a series of lignans is analysed using the in silico-developed models, and in vitro corroboration of the activity is carried out. An issue of great importance to remark here is that all compounds present greater inhibition values than Kojic acid (standard tyrosinase inhibitor: IC₅₀ = 16.67 μm). The current obtained results could be used as a framework to increase the speed, in the biosilico discovery of leads for the treatment of skin disorders.

Beyond the oncogene paradigm: understanding complexity in cancerogenesis

In the past decades, an enormous amount of precious information has been collected about molecular and genetic characteristics of cancer. This knowledge is mainly based on a reductionistic approach, meanwhile cancer is widely recognized to be a 'system biology disease'. The behavior of complex physiological processes cannot be understood simply by knowing how the parts work in isolation. There is not solely a matter how to integrate all available knowledge in such a way that we can still deal with complexity, but we must be aware that a deeply transformation of the currently accepted oncologic paradigm is urgently needed. We have to think in terms of biological networks: understanding of complex functions may in fact be impossible without taking into consideration influences (rules and constraints) outside of the genome. Systems Biology involves connecting experimental unsupervised multivariate data to mathematical and computational approach than can simulate biologic systems for hypothesis testing or that can account for what it is not known from high-throughput data sets. Metabolomics could establish the requested link between genotype and phenotype, providing informations that ensure an integrated understanding of pathogenic mechanisms and metabolic phenotypes and provide a screening tool for new targeted drug.

Prediction of tyrosinase inhibition activity using atom-based bilinear indices

A set of novel atom-based molecular fingerprints is proposed based on a bilinear map similar to that defined in linear algebra. These molecular descriptors (MDs) are proposed as a new means of molecular parametrization easily calculated from 2D molecular information. The nonstochastic and stochastic molecular indices match molecular structure provided by molecular topology by using the kth nonstochastic and stochastic graph-theoretical electronic-density matrices, M(k) and S(k), respectively. Thus, the kth nonstochastic and stochastic bilinear indices are calculated using M(k) and S(k) as matrix operators of bilinear transformations. Chemical information is coded by using different pair combinations of atomic weightings (mass, polarizability, vdW volume, and electronegativity). The results of QSAR studies of tyrosinase inhibitors using the new MDs and linear discriminant analysis (LDA) demonstrate the ability of the bilinear indices in testing biological properties. A database of 246 structurally diverse tyrosinase inhibitors was assembled. An inactive set of 412 drugs with other clinical uses was used; both active and inactive sets were processed by hierarchical and partitional cluster analyses to design training and predicting sets. Twelve LDA-based QSAR models were obtained, the first six using the nonstochastic total and local bilinear indices and the last six with the stochastic MDs. The discriminant models were applied; globally good classifications of 99.58 and 89.96 % were observed for the best nonstochastic and stochastic bilinear indices models in the training set along with high Matthews correlation coefficients (C) of 0.99 and 0.79, respectively, in the learning set. External prediction sets used to validate the models obtained were correctly classified, with accuracies of 100 and 87.78 %, respectively, yielding C values of 1.00 and 0.73. This subset contains 180 active and inactive compounds not considered to fit the models. A simulated virtual screen demonstrated this approach in searching tyrosinase inhibitors from compounds never considered in either training or predicting series. These fitted models permitted the selection of new cycloartane compounds isolated from herbal plants as new tyrosinase inhibitors. A good correspondence between theoretical and experimental inhibitory effects on tyrosinase was observed; compound CA6 (IC(50)=1.32 microM) showed higher activity than the reference compounds kojic acid (IC(50)=16.67 microM) and L-mimosine (IC(50)=3.68 microM).

Lead-like, drug-like or "Pub-like": how different are they?

Academic and industrial research continues to be focused on discovering new classes of compounds based on HTS. Post-HTS analyses need to prioritize compounds that are progressed to chemical probe or lead status. We report trends in probe, lead and drug discovery by examining the following categories of compounds: 385 leads and the 541 drugs that emerged from them; "active" (152) and "inactive" (1488) compounds from the Molecular Libraries Initiative Small Molecule Repository (MLSMR) tested by HTS; "active" (46) and "inactive" (72) compounds from Nature Chemical Biology (NCB) tested by HTS; compounds in the drug development phase (I, II, III and launched), as indexed in MDDR; and medicinal chemistry compounds from WOMBAT, separated into high-activity (5,784 compounds with nanomolar activity or better) and low-activity (30,690 with micromolar activity or less). We examined Molecular weight (MW), molecular complexity, flexibility, the number of hydrogen bond donors and acceptors, LogP-the octanol/water partition coefficient estimated by ClogP and ALOGPS), LogSw (intrinsic water solubility, estimated by ALOGPS) and the number of Rule of five (Ro5) criteria violations. Based on the 50% and 90% distribution moments of the above properties, there were no significant difference between leads of known drugs and "actives" from MLSMR or NCB (chemical probes). "Inactives" from NCB and MLSMR were also found to exhibit similar properties. From these combined sets, we conclude that "Actives" (569 compounds) are less complex, less flexible, and more soluble than drugs (1,651 drugs), and significantly smaller, less complex, less hydrophobic and more soluble than the 5,784 high-activity WOMBAT compounds. These trends indicate that chemical probes are similar to leads with respect to some properties, e.g., complexity, solubility, and hydrophobicity.

Pharmacogenetics: the bioethical problem of DNA investment banking

Concern about the ethics of clinical drug trials research on patients and healthy volunteers has been the subject of significant ethical analysis and policy development--protocols are reviewed by Research Ethics Committees and subjects are protected by informed consent procedures. More recently attention has begun to be focused on DNA banking for clinical and pharmacogenetics research. It is, however, surprising how little attention has been paid to the commercial nature of such research, or the unique issues that present when subjects are asked to consent to the storage of biological samples. Our contention is that in the context of pharmacogenetic add-on studies to clinical drug trials, the doctrine of informed consent fails to cover the broader range of social and ethical issues. Applying a sociological perspective, we foreground issues of patient/subject participation or 'work', the ambiguity of research subject altruism, and the divided loyalties facing many physicians conducting clinical research. By demonstrating the complexity of patient and physician involvement in clinical drug trials, we argue for more comprehensive ethical review and oversight that moves beyond reliance on informed consent to incorporate understandings of the social, political and cultural elements that underpin the diversity of ethical issues arising in the research context.

Fragment screening: an introduction

There are clearly many different philosophies associated with adapting fragment screening into mainstream Drug Discovery Lead Generation strategies. Scientists at Astex, for instance, focus entirely on strategies involving use of X-ray crystallography and NMR. However, AstraZeneca uses a number of different fragment screening strategies. One approach is to screen a 2000 compound fragment set (with close to "lead-like" complexity) at 100 microM in parallel with every HTS such that the data are obtained on the entire screening collection at 10 microM plus the extra samples at 100 microM; this provides valuable compound potency data in a concentration range that is usually unexplored. The fragments are then screen-specific "privileged structures" that can be searched for in the rest of the HTS output and other databases as well as having synthesis follow-up. A typical workflow for a fragment screen within AstraZeneca is shown below (Figure 24) and highlights the desirability (particularly when screening >100 microM) for NMR and X-ray information to validate weak hits and give information on how to optimise them. In this chapter, we have provided an introduction to the theoretical and practical issues associated with the use of fragment methods and lead-likeness. Fragment-based approaches are still in an early stage of development and are just one of many interrelated techniques that are now used to identify novel lead compounds for drug development. Fragment based screening has some advantages, but like every other drug hunting strategy will not be universally applicable. There are in particular some practical challenges associated with fragment screening that relate to the generally lower level of potency that such compounds initially possess. Considerable synthetic effort has to be applied for post-fragment screening to build the sort of potency that would be expected to be found from a traditional HTS. However, if there are no low-hanging fruit in a screening collection to be found by HTS then the use of fragment screening can help find novelty that may lead to a target not being discarded as intractable. As such, the approach offers some significant advantages by providing less complex molecules, which may have better potential for novel drug optimisation and by enabling new chemical space to be more effectively explored. Many literature examples that cover examples of fragment screening approaches are still at the "proof of concept" stage and although delivering inhibitors or ligands, may still prove to be unsuitable when further ADMET and toxicity profiling is done. The next few years should see a maturing of the area, and as our understanding of how the concepts can be best applied, there are likely to be many more examples of attractive, small molecule hits, leads and candidate drugs derived from the approaches described.

Genomics, molecular targets and the discovery of antifungal drugs

Comparative analyses of fungal genomes and molecular research on genes associated with fungal viability and virulence has led to the identification of many putative targets for novel antifungal agents. So far the rational approach to antifungal discovery, in which compounds are optimized against an individual target then progressed to efficacy against intact fungi and ultimately to infected humans has delivered no new agents. However, the approach continues to hold promise for the future. This review critically assesses the molecular target-based approach to antifungal discovery, outlines problems and pitfalls inherent in the genomics and target discovery strategies and describes the status of heavily investigated examples of target-based research.

Integrating virtual screening in lead discovery

Target- and ligand-based virtual screening have emerged as resource-saving techniques that have been successfully applied to identify novel chemotypes in biologically active molecules. Eight confirmed virtual screening hits have recently been described and are discussed in this review, with focus on the workflow. These are then evaluated in the light of pharmacokinetics prediction (e.g. Caco-2 permeability, cytochrome P450 inhibition and hERG binding). We anticipate problems for five of these hits (e.g. cardiac toxicity), which warrant further experiments. Future challenges include dynamic tautomer/protonation treatment for both ligands and targets and improved pre- and post- virtual screening filters.

Atom, atom-type and total molecular linear indices as a promising approach for bioorganic and medicinal chemistry: theoretical and experimental assessment of a novel method for virtual screening and rational design of new lead anthelmintic

Helminth infections are a medical problem in the world nowadays. In this paper a novel atom-level chemical descriptor has been applied to estimate the anthelmintic activity. Total and local linear indices and linear discriminant analysis were used to obtain a quantitative model that discriminates between anthelmintic and non-anthelmintic drug-like compounds. The discriminant model has an accuracy of 90.11% in the training set, with a high Matthews' correlation coefficient (MCC=0.80). To assess the robustness and predictive power of the obtained model, internal (leave-n-out) and external validation process was performed. The QSAR model correctly classified 88.55% of compounds in this external prediction set, yielding a MCC of 0.77. Another LDA model was carried out to outline some conclusions about the possible modes of action of anthelmintic drugs. It has an accuracy of 93.50% in the training set, and 80.00% in the external prediction set. After that, the developed model was used in the virtual--in silico--screening and several compounds from the Merck Index, Negwer's Handbook and Goodman and Gilman were identified by the model as anthelmintic. Finally, the experimental assay of an organic chemical (a furylethylene derivative) by an in vivo test permits us to carry out an assessment of the model. An accuracy of 100% with the theoretical predictions was observed. These results suggest that the proposed method will be a good tool for studying the biological properties of drug candidates during the early state of the drug-development process.

Pursuing the leadlikeness concept in pharmaceutical research

Lipinski and others, through concepts such as drug-likeness, re-focussed drug discovery back to the principles of medicinal chemistry in the high-throughput era as key to reducing attrition. More recently, the need to go further in defining what makes a good lead has been recognised with the concept of leadlikeness. Leadlikeness implies cut-off values in the physico-chemical profile of chemical libraries such that they have reduced complexity (e.g. MW below <400) and other more restricted properties. We examine these concepts in the context of Virtual (theoretically possible), Tangible (chemically feasible) and Real (physically available) worlds of molecules. In a thought experiment, we take the HTS concept to the extreme: screening an estimated 60 million 'Global Collection' on 5000 targets and realising that perhaps millions of drug candidates might be found that could not possibly be handled in reality. Sampling of the Virtual and Tangible worlds is therefore a necessity. We show that the world of Reals is significantly under-sampled as the MW of compounds increases. This supports the design and screening of 'reduced complexity' (leadlike) compound libraries, preferably with synthetic handles available for rapid chemical iteration and detected as interesting by careful screening or biophysical assays.

Medical research funding may have over-expanded and be due for collapse

The continual and uninterrupted expansion of medical research funding is generally assumed to be a permanent feature of modern societies, but this expectation may turn out to be mistaken. Sciences tend to go through boom and bust phases. Twentieth century physics is an example where huge increases in funding followed an era of scientific breakthroughs. Speculative over-expansion led to diminishing returns on investment, then a collapse in funding. We predict that medicine will follow the same trajectory. After prolonged over-funding of the 'basic-to-applied' model of clinical innovation, and a progressive shift towards Big Science organization, medical research has become increasingly inefficient and ineffective. Although incremental improvements to existing treatment strategies continue, the rate of significant therapeutic breakthroughs has been declining for three decades. Medical science now requires rationalization and modernization. From this perspective, the current level of medical research funding looks like a bubble due to burst.

The therapeutic implications of ketone bodies: the effects of ketone bodies in pathological conditions: ketosis, ketogenic diet, redox states, insulin resistance, and mitochondrial metabolism

The effects of ketone body metabolism suggests that mild ketosis may offer therapeutic potential in a variety of different common and rare disease states. These inferences follow directly from the metabolic effects of ketosis and the higher inherent energy present in d-beta-hydroxybutyrate relative to pyruvate, the normal mitochondrial fuel produced by glycolysis leading to an increase in the DeltaG' of ATP hydrolysis. The large categories of disease for which ketones may have therapeutic effects are:(1)diseases of substrate insufficiency or insulin resistance,(2)diseases resulting from free radical damage,(3)disease resulting from hypoxia. Current ketogenic diets are all characterized by elevations of free fatty acids, which may lead to metabolic inefficiency by activation of the PPAR system and its associated uncoupling mitochondrial uncoupling proteins. New diets comprised of ketone bodies themselves or their esters may obviate this present difficulty.

Rhetoric and hype: where's the 'ethics' in pharmacogenomics?

There is increasing discussion in public and academic forums about the anticipated benefits of pharmacogenomics, as well as the attendant social and ethical implications of this research. Yet there is often an implicit assumption that the benefits of pharmacogenomics are 'just around the corner' and will significantly outweigh the costs. Furthermore, it is argued that the associated ethical issues are not as profound as those that emerge in other areas of genetics, and that experience gained wrestling with these other issues provides ample ethical and regulatory tools to deal with any problems arising with pharmacogenomics. We contend that this vision of ethical and social issues associated with pharmacogenomics is not so clear-cut. The scientific evidence is more complex and contested than the public, academics, and policy makers, have been led to believe, and while there may be real clinical benefits from this research, they are not likely to arrive in the near future. Pharmacogenomics research is also occurring in a terrain occupied by a multitude of different and powerful actors, with diverse and often competing interests. It is therefore essential to investigate the broader social and political context, unravel the various interests pressuring for early implementation, and deconstruct the hype in order to appreciate a fuller range of ethical and social consequences associated with the current developments of pharmacogenomics.

Quest for the best

By combining innovative science with years of massive investment, drug makers seek to turn newly discovered chemicals into revolutionary blockbuster drugs that generate billions of dollars in revenue. So every year, they collectively spend tens of billions of dollars on the high-risk pursuit of the next Prozac or Viagra. But should they? This article analyses key themes around differentiation that we have found to be common among blockbusters, and examines the implications for creating future billion-dollar drugs.

Topological virtual screening: a way to find new anticonvulsant drugs from chemical diversity

A topological virtual screening (tvs) test is presented, which is capable of identifying new drug leaders with anticonvulsant activity. Molecular structures of both anticonvulsant-active and non active compounds, extracted from the Merck Index database, were represented using topological indexes. By means of the application of a linear discriminant analysis to both sets of structures, a topological anticonvulsant model (tam) was obtained, which defines a connectivity function. On the basis of this model, 41 new structures with anticonvulsant activity have been identified by a topological virtual screening.

Current trends in lead discovery: are we looking for the appropriate properties?

The new drug discovery paradigm is based on high-throughput technologies, both with respect to synthesis and screening. The progression HTS hits --> lead series --> candidate drug --> marketed drug appears to indicate that the probability of reaching launched status is one in a million. This has shifted the focus from good quality candidate drugs to good quality leads. We examined the current trends in lead discovery by comparing MW (molecular weight), LogP (octanol/water partition coefficient, estimated by Kowwin) and LogSw (intrinsic water solubility, estimated by Wskowwin) for the following categories: 62 leads and 75 drugs; compounds in the development phase (I, II, III and launched), as indexed in MDDR; and compounds indexed in medicinal chemistry journals, categorized according to their biological activity. Comparing the distribution of the above properties, the 62 lead structures show the lowest median with respect to MW (smaller) and LogP (less hydrophobic), and the highest median with respect to LogSw (more soluble). By contrast, over 50% of the medicinal chemistry compounds with activities above 1 nanomolar have MW > 425, LogP > 4.25 and LogSw < -4.75, indicating that the reported active compounds are larger, more hydrophobic and lesssoluble when compared to time-tested quality leads. In the MDDR set, a progressive constraint to reduce MW and LogP, and to increase LogSw, can be observed when examining trends in the developmental sequence: phase I, II, III and launched drugs. These trends indicate that other properties besides binding affinity, e.g., solubility and hydrophobicity, need to be considered when choosing the appropriate leads.

Chemical space navigation in lead discovery

The number of new chemical entities has remained rather constant (averaging 37 per year) in the past decade, despite the multiple-fold increase in the number of compounds that are being made and tested. Chemical space requires novel methods that can handle the increasing number of potentially accessible molecules. Neighborhood behavior, as an approach to similarity, and chemical property space navigation are some of the recent advances that are discussed, in the context of lead discovery and appropriate pharmacokinetic properties.

Current trends in lead discovery: are we looking for the appropriate properties?

The new drug discovery paradigm is based on high-throughput technologies, both with respect to synthesis and screening. The progression HTS hits --> lead series --> candidate drug --> marketed drug appears to indicate that the probability of reaching launched status is one in a million. This has shifted the focus from good quality candidate drugs to good quality leads. We examined the current trends in lead discovery by comparing MW (molecular weight), LogP (octanol/water partition coefficient, estimated by Kowwin) and LogSw (intrinsic water solubility, estimated by Wskowwin) for the following categories: 62 leads and 75 drugs; compounds in the development phase (I, II, III and launched), as indexed in MDDR; and compounds indexed in medicinal chemistry journals, categorized according to their biological activity. Comparing the distribution of the above properties, the 62 lead structures show the lowest median with respect to MW (smaller) and LogP (less hydrophobic), and the highest median with respect to LogSw (more soluble). By contrast, over 50% of the medicinal chemistry compounds with activities above 1 nanomolar have MW > 425, LogP > 4.25 and LogSw < -4.75, indicating that the reported active compounds are larger, more hydrophobic and less soluble when compared to time-tested quality leads. In the MDDR set, a progressive constraint to reduce MW and LogP, and to increase LogSw, can be observed when examining trends in the developmental sequence: phase I, II, III and launched drugs. These trends indicate that other properties besides binding affinity, e.g., solubility and hydrophobicity, need to be considered when choosing the appropriate leads.