The impact of genomics on drug discovery

Deep Video Analysis for Bacteria Genotype Prediction

Genetic modification of microbes is central to many biotechnology fields, such as industrial microbiology, bioproduction, and drug discovery. Understanding how specific genetic modifications influence observable bacterial behaviors is crucial for advancing these fields. In this study, we propose a supervised model to classify bacteria harboring single gene modifications to draw connections between phenotype and genotype. In particular, we demonstrate that the spatiotemporal patterns of Vibrio cholerae growth, recorded in terms of low-resolution bright-field microscopy videos, are highly predictive of the genotype class. Additionally, we introduce a weakly supervised approach to identify key moments in culture growth that significantly contribute to prediction accuracy. By focusing on the temporal expressions of bacterial behavior, our findings offer valuable insights into the underlying mechanisms and developmental stages by which specific genes control observable phenotypes. This research opens new avenues for automating the analysis of phenotypes, with potential applications for drug discovery, disease management, etc. Furthermore, this work highlights the potential of using machine learning techniques to explore the functional roles of specific genes using a low-resolution light microscope.

Bioinformatics in Neonatal/Pediatric Medicine-A Literature Review

Bioinformatics is a scientific field that uses computer technology to gather, store, analyze, and share biological data and information. DNA sequences of genes or entire genomes, protein amino acid sequences, nucleic acid, and protein-nucleic acid complex structures are examples of traditional bioinformatics data. Moreover, proteomics, the distribution of proteins in cells, interactomics, the patterns of interactions between proteins and nucleic acids, and metabolomics, the types and patterns of small-molecule transformations by the biochemical pathways in cells, are further data streams. Currently, the objectives of bioinformatics are integrative, focusing on how various data combinations might be utilized to comprehend organisms and diseases. Bioinformatic techniques have become popular as novel instruments for examining the fundamental mechanisms behind neonatal diseases. In the first few weeks of newborn life, these methods can be utilized in conjunction with clinical data to identify the most vulnerable neonates and to gain a better understanding of certain mortalities, including respiratory distress, bronchopulmonary dysplasia, sepsis, or inborn errors of metabolism. In the current study, we performed a literature review to summarize the current application of bioinformatics in neonatal medicine. Our aim was to provide evidence that could supply novel insights into the underlying mechanism of neonatal pathophysiology and could be used as an early diagnostic tool in neonatal care.

Structural determination and kinetic analysis of the transketolase from Vibrio vulnificus reveal unexpected cooperative behavior

Vibrio vulnificus (vv) is a multidrug-resistant human bacterial pathogen whose prevalence is expected to increase over the years. Transketolases (TK), transferases catalyzing two reactions of the nonoxidative branch of the pentose-phosphate pathway and therefore linked to several crucial metabolic pathways, are potential targets for new drugs against this pathogen. Here, the vvTK is crystallized and its structure is solved at 2.1 Å. A crown of 6 histidyl residues is observed in the active site and expected to participate in the thiamine pyrophosphate (cofactor) activation. Docking of fructose-6-phosphate and ferricyanide used in the activity assay, suggests that both substrates can bind vvTK simultaneously. This is confirmed by steady-state kinetics showing a sequential mechanism, on the contrary to the natural transferase reaction which follows a substituted mechanism. Inhibition by the I38-49 inhibitor (2-(4-ethoxyphenyl)-1-(pyrimidin-2-yl)-1H-pyrrolo[2,3-b]pyridine) reveals for the first time a cooperative behavior of a TK and docking experiments suggest a previously undescribed binding site at the interface between the pyrophosphate and pyridinium domains.

Early clinical trial unit tumor board: a real-world experience in a national cancer network

PURPOSE

Early clinical trials are the first step into clinical therapies for new drugs. Within the six Bavarian university-based hospitals (Augsburg, Erlangen, Regensburg, Munich (LMU and TU), Würzburg) we have enrolled a virtual network platform for patient discussion.

METHODS

The virtual Early Clinical Trial Unit Tumor Board (ECTU Tumor Board) is a secured web-based meeting to evaluate early clinical trial options for patients, where representatives from local ECTUs participate. We retrospectively analyzed patient cases discussed between November 2021 and November 2022.

RESULTS

From November 2021 to November 2022, a total of 43 patients were discussed in the ECTU Tumor Board. Median age at diagnosis was 44.6 years (range 10-76 years). The median number of previous lines of therapies was 3.7 (range 1-9 therapies) including systemic treatment, surgery, and radiation therapy. A total of 27 different tumor entities were presented and 83.7% (36/43) patients received at least one trial recommendation. In total, 21 different active or shortly recruiting clinical trials were recommended: ten antibody trials, four BiTE (bispecific T cell engager) trials, six CAR (chimeric antigen receptor) T-cell trials, and one chemotherapy trial. Only six trials (28.6%) were recommended on the basis of the previously performed comprehensive genetic profiling (CGP).

CONCLUSION

The ECTU Tumor Board is a feasible and successful network, highlighting the force of virtual patient discussions for improving patient care as well as trial recruitment in advanced diseases. It can provide further treatment options after local MTB presentation, aiming to close the gap to access clinical trials.

The Role of Natural Products as Sources of Therapeutic Agents for Innovative Drug Discovery

Emerging threats to human health require a concerted effort in search of both preventive and treatment strategies, placing natural products at the center of efforts to obtain new therapies and reduce disease spread and associated mortality. The therapeutic value of compounds found in plants has been known for ages, resulting in their utilization in homes and in clinics for the treatment of many ailments ranging from common headache to serious conditions such as wounds. Despite the advancement observed in the world, plant based medicines are still being used to treat many pathological conditions or are used as alternatives to modern medicines. In most cases, these natural products or plant-based medicines are used in an un-purified state as extracts. A lot of research is underway to identify and purify the active compounds responsible for the healing process. Some of the current drugs used in clinics have their origins as natural products or came from plant extracts. In addition, several synthetic analogues are natural product-based or plant-based. With the emergence of novel infectious agents such as the SARS-CoV-2 in addition to already burdensome diseases such as diabetes, cancer, tuberculosis and HIV/AIDS, there is need to come up with new drugs that can cure these conditions. Natural products offer an opportunity to discover new compounds that can be converted into drugs given their chemical structure diversity. Advances in analytical processes make drug discovery a multi-dimensional process involving computational designing and testing and eventual laboratory screening of potential drug candidates. Lead compounds will then be evaluated for safety, pharmacokinetics and efficacy. New technologies including Artificial Intelligence, better organ and tissue models such as organoids allow virtual screening, automation and high-throughput screening to be part of drug discovery. The use of bioinformatics and computation means that drug discovery can be a fast and efficient process and enable the use of natural products structures to obtain novel drugs. The removal of potential bottlenecks resulting in minimal false positive leads in drug development has enabled an efficient system of drug discovery. This review describes the biosynthesis and screening of natural products during drug discovery as well as methods used in studying natural products.

Calcium Oscillation Frequency Is a Potential Functional Complex Physiological Relevance Indicator for a Neuroblastoma-Based 3D Culture Model

In vitro screening for drugs that affect neural function in vivo is still primitive. It primarily relies on single cellular responses from 2D monolayer cultures that have been shown to be exaggerations of the in vivo response. For the 3D model to be physiologically relevant, it should express characteristics that not only differentiate it from 2D but also closely emulate those seen in vivo. These complex physiologically relevant (CPR) outcomes can serve as a standard for determining how close a 3D culture is to its native tissue or which out of a given number of 3D platforms is better suited for a given application. In this study, Fluo-4-based calcium fluorescence imaging was performed followed by automated image data processing to quantify the calcium oscillation frequency of SHSY5Y cells cultured in 2D and 3D formats. It was found that the calcium oscillation frequency is upregulated in traditional 2D cultures while it was comparable to in vivo in spheroid and microporous polymer scaffold-based 3D models, suggesting calcium oscillation frequency as a potential functional CPR indicator for neural cultures.

A perspective on the discovery of selected compounds with anthelmintic activity against the barber's pole worm-Where to from here?

Parasitic roundworms (nematodes) cause substantial morbidity and mortality in animals worldwide. Anthelmintic treatment is central to controlling these worms, but widespread resistance to most of the commercially available anthelmintics for veterinary and agricultural use is compromising control, such that there is an urgency to discover new and effective drugs. The purpose of this article is to review information on parasitic nematodes, the treatment and control of parasitic nematode infections and aspects of discovering new anthelmintics in the context of anthelmintic resistance problems, and then to discuss some progress that our group has made in identifying selected compounds with activity against nematodes. The focus of our recent work has been on discovering new chemical entities and known drugs with anthelmintic activities against Haemonchus contortus as well as other socioeconomically important parasitic nematodes for subsequent development. Using whole worm-based phenotypic assays, we have been screening compound collections obtained via product-development-partnerships and/or collaborators, and active compounds have been assessed for their potential as anthelmintic candidates. Following the screening of 15,333 chemicals from five distinct compound collections against H. contortus, we have discovered one new chemical entity (designated SN00797439), two human kinase inhibitors (SNS-032 and AG-1295), 14 tetrahydroquinoxaline analogues, one insecticide (tolfenpyrad) and two tolfenpyrad (pyrazole-5-carboxamide) derivatives (a-15 and a-17) with anthelmintic activity in vitro. Some of these 20 'hit' compounds have selectivity against H. contortus in vitro when compared to particular human cell lines. In our opinion, some of these compounds could represent starting points for 'lead' development. Accordingly, the next research steps to be pursued include: (i) chemical optimisation of representative chemicals via structure-activity relationship (SAR) evaluations; (ii) assessment of the breadth of spectrum of anthelmintic activity on a range of other parasitic nematodes, such as strongyloids, ascaridoids, enoplids and filarioids; (iii) detailed investigations of the absorption, distribution, metabolism, excretion and toxicity (ADMET) of optimised chemicals with broad nematocidal or nematostatic activity; and (iv) establishment of the modes of action of lead candidates.

Effect of Environmental Exposure and Pharmacogenomics on Drug Metabolism

BACKGROUND

Pesticides are major xenobiotic compounds and environmental pollutants, which are able to alter drug-metabolizing enzyme as well as pharmacokinetics of drugs. Subsequent to the release of the human genome project, genetic variations (polymorphism) become an integral part of drug development due to their influence on disease susceptibility/ progression of the disease and their impact on drug absorption, distribution, metabolism of active metabolites and finally excretion of the drug. Genetic polymorphisms crucially regulate pharmacokinetics and pharmacodynamics of drugs under the influence of physiological condition, lifestyle, as well as pathological conditions collectively.

OBJECTIVE

To review all the evidence concerning the effect of environmental exposure on drug metabolism with reference to pharmacogenomics.

METHODS

Scientific data search and review of basic, epidemiological, pharmacogenomics and pharmacokinetics studies were undertaken to evaluate the influence of environmental contaminants on drug metabolism.

RESULTS

Various environmental contaminants like pesticides effectively alter drug metabolism at various levels under the influence of pharmacogenomics, which interferes with pharmacokinetics of drug metabolism. Genetic polymorphism of phase I and phase II xenobiotic-metabolizing enzymes remarkably alters disease susceptibility as well as the progression of disease under the influence of various environmental contaminants at various levels.

CONCLUSION

Individual specific drug response may be attributed to a large variety of factors alone or in combination ranging from genetic variations (SNP, insertion, deletion, duplication etc.) to physiological setting (gender, age, body size, and ethnicity), environmental or lifestyle factors (radiation exposure, smoking, alcohol, nutrition, exposure to toxins, etc.); and pathological conditions (obesity, diabetes, liver and renal function).

Protein-protein interaction modulators: advances, successes and remaining challenges

Modulating disease-relevant protein-protein interactions (PPIs) using small-molecule inhibitors is a quite indispensable diagnostic and therapeutic strategy in averting pathophysiological cues and disease progression. Over the years, targeting intracellular PPIs as drug design targets has been a challenging task owing to their highly dynamic and expansive interfacial areas (flat, featureless and relatively large). However, advances in PPI-focused drug discovery technology have been reported and a few drugs are already on the market, with some potential drug-like candidates already in clinical trials. In this article, we review the advances, successes and remaining challenges in the application of small molecules as valuable PPI modulators in disease diagnosis and therapeutics.

Combined Scaffold Evaluation and Systems-Level Transcriptome-Based Analysis for Accelerated Lead Optimization Reveals Ribosomal Targeting Spirooxindole Cyclopropanes

With evolutionary drug resistance impacting efforts to treat disease, the need for small molecules that exhibit novel molecular mechanisms of action is paramount. In this study, we combined scaffold-directed synthesis with a hybrid experimental and transcriptome analysis to identify bis-spirooxindole cyclopropanes that inhibit cancer cell proliferation through disruption of ribosomal function. These findings demonstrate the value of an integrated, biologically inspired synthesis and assay strategy for the accelerated identification of first-in-class cancer therapeutic candidates.

Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery

The therapeutic properties of plants have been recognised since time immemorial. Many pathological conditions have been treated using plant-derived medicines. These medicines are used as concoctions or concentrated plant extracts without isolation of active compounds. Modern medicine however, requires the isolation and purification of one or two active compounds. There are however a lot of global health challenges with diseases such as cancer, degenerative diseases, HIV/AIDS and diabetes, of which modern medicine is struggling to provide cures. Many times the isolation of "active compound" has made the compound ineffective. Drug discovery is a multidimensional problem requiring several parameters of both natural and synthetic compounds such as safety, pharmacokinetics and efficacy to be evaluated during drug candidate selection. The advent of latest technologies that enhance drug design hypotheses such as Artificial Intelligence, the use of 'organ-on chip' and microfluidics technologies, means that automation has become part of drug discovery. This has resulted in increased speed in drug discovery and evaluation of the safety, pharmacokinetics and efficacy of candidate compounds whilst allowing novel ways of drug design and synthesis based on natural compounds. Recent advances in analytical and computational techniques have opened new avenues to process complex natural products and to use their structures to derive new and innovative drugs. Indeed, we are in the era of computational molecular design, as applied to natural products. Predictive computational softwares have contributed to the discovery of molecular targets of natural products and their derivatives. In future the use of quantum computing, computational softwares and databases in modelling molecular interactions and predicting features and parameters needed for drug development, such as pharmacokinetic and pharmacodynamics, will result in few false positive leads in drug development. This review discusses plant-based natural product drug discovery and how innovative technologies play a role in next-generation drug discovery.

Drug development in the era of precision medicine

For the past three decades, the use of genomics to inform drug discovery and development pipelines has generated both excitement and scepticism. Although earlier efforts successfully identified some new drug targets, the overall clinical efficacy of developed drugs has remained unimpressive, owing in large part to the heterogeneous causes of disease. Recent technological and analytical advances in genomics, however, have now made it possible to rapidly identify and interpret the genetic variation underlying a single patient's disease, thereby providing a window into patient-specific mechanisms that cause or contribute to disease, which could ultimately enable the 'precise' targeting of these mechanisms. Here, we first examine and highlight the successes and limitations of the earlier phases of genomics in drug discovery and development. We then review the current major efforts in precision medicine and discuss the potential broader utility of mechanistically guided treatments going forward.

"Omics"-Informed Drug and Biomarker Discovery: Opportunities, Challenges and Future Perspectives

The pharmaceutical industry faces unsustainable program failure despite significant increases in investment. Dwindling discovery pipelines, rapidly expanding R&D budgets and increasing regulatory control, predict significant gaps in the future drug markets. The cumulative duration of discovery from concept to commercialisation is unacceptably lengthy, and adds to the deepening crisis. Existing animal models predicting clinical translations are simplistic, highly reductionist and, therefore, not fit for purpose. The catastrophic consequences of ever-increasing attrition rates are most likely to be felt in the developing world, where resistance acquisition by killer diseases like malaria, tuberculosis and HIV have paced far ahead of new drug discovery. The coming of age of Omics-based applications makes available a formidable technological resource to further expand our knowledge of the complexities of human disease. The standardisation, analysis and comprehensive collation of the "data-heavy" outputs of these sciences are indeed challenging. A renewed focus on increasing reproducibility by understanding inherent biological, methodological, technical and analytical variables is crucial if reliable and useful inferences with potential for translation are to be achieved. The individual Omics sciences-genomics, transcriptomics, proteomics and metabolomics-have the singular advantage of being complimentary for cross validation, and together could potentially enable a much-needed systems biology perspective of the perturbations underlying disease processes. If current adverse trends are to be reversed, it is imperative that a shift in the R&D focus from speed to quality is achieved. In this review, we discuss the potential implications of recent Omics-based advances for the drug development process.

Circular Cone: a novel approach for protein ligand shape matching using modified PCA

Nowadays in modern medicine, computer modeling has already become one of key methods toward the discovery of new pharmaceuticals. And virtual screening is a necessary process for this discovery. In the procedure of virtual screening, shape matching is the first step to select ligands for binding protein. In the era of HTS (high throughput screening), a fast algorithm with good result is in demand. Many methods have been discovered to fulfill the requirement. Our method, called "Circular Cone", by finding principal axis, gives another way toward this problem. We use modified PCA (principal component analysis) to get the principal axis, around which the rotation is like whirling a cone. By using this method, the speed of giving score to a pocket and a ligand is very fast, while the accuracy is ordinary. So, the good speed and the general accuracy of our method present a good choice for HTS.

Role of ATP binding and hydrolysis in the gating of the cystic fibrosis transmembrane conductance regulator

The CFTR gene is unique within the ATP-binding cassette (ABC) protein family, predominantly of transporters, by coding a chloride channel. The gating mechanism of ABC proteins has been characterized by the ATP Switch model in terms cycles of dimer formation and dissociation linked to ATP binding and hydrolysis, respectively. It would be of interest to assess the extent that Cystic Fibrosis Transmembrane Conductance Regulator (CFTR), a functional channel, fits the ATP Switch model for ABC transporters. Additional transporter mechanisms, namely those of Pgp and HlyB, are discussed for perspective. Literature search of databases selected key references in comparing and contrasting the gating mechanism. CFTR is a functional chloride channel facilitating transmembrane anion flow down electrochemical gradients. A dysfunctional CFTR protein results in cystic fibrosis, a fatal pleiotropic disease currently managed symptomatically. Understanding the gating mechanism will help target drug development aimed at alleviating and curing the disease.

The research strategies for probing the function of long noncoding RNAs

Long noncoding RNAs (lncRNAs) represent a new frontier in molecular genetics and molecular biology. They have a tremendous potential for advancing our comprehensive understanding of biological processes in huma n health and disease. The transcripts of lncRNAs are easy to find, but sorting out what they do remains the biggest challenge in lncRNAs' research field. In the paper, we highlight recent progress regarding the methods to explore the roles of lncRNAs.

A decade of the human genome sequence--how does the medicinal chemist benefit?

Many have claimed that the sequencing of the human genome has failed to deliver the promised new era of drug discovery and development. Here, we argue that in fact, the availability of the human genome sequence and the genomics technologies that resulted from those research efforts have had a major impact on drug discovery. Medicinal chemists are actively using the data gleaned from structural genomics projects over the past decade to design more selective and more effective drug candidates. For example, large superfamilies of related enzymes, such as the kinome, proteome, proteasome, transportome, identified because of the sequencing of the human genome represent a huge number of potential drug targets. Ten years on, we're able to design multitarget drugs where the selectivity for a certain subgroup of receptors can lead to increased efficacy rather than the side effects traditionally associated with "off-targets". New trends and discoveries in biomedical research are notoriously slow to show their value, and this is also true for genomics technologies. However, the examples we've selected show that these are firmly set in the drug-discovery process, and without the human genome sequence, a number of current clinical candidates and promising drug leads would not have been possible.

High-throughput biosensors for multiplexed food-borne pathogen detection

Incidental contamination of foods by pathogenic bacteria and/or their toxins is a serious threat to public health and the global economy. The presence of food-borne pathogens and toxins must be rapidly determined at various stages of food production, processing, and distribution. Producers, processors, regulators, retailers, and public health professionals need simple and cost-effective methods to detect different species or serotypes of bacteria and associated toxins in large numbers of food samples. This review addresses the desire to replace traditional microbiological plate culture with more timely and less cumbersome rapid, biosensor-based methods. Emphasis focuses on high-throughput, multiplexed techniques that allow for simultaneous testing of numerous samples, in rapid succession, for multiple food-borne analytes (primarily pathogenic bacteria and/or toxins).

Applications of genetically modified tools to safety assessment in drug development

The process of new drug development consists of several stages; after identifying potential candidate compounds, preclinical studies using animal models link the laboratory and human clinical trials. Among many steps in preclinical studies, toxicology and safety assessments contribute to identify potential adverse events and provide rationale for setting the initial doses in clinical trials. Gene modulation is one of the important tools of modern biology, and is commonly employed to examine the function of genes of interest. Advances in new drug development have been achieved by exploding information on target selection and validation using genetically modified animal models as well as those of cells. In this review, a recent trend of genetically modified methods is discussed with reference to safety assessments, and the exemplary applications of gene-modulating tools to the tests in new drug development were summarized.

Exploiting complexity and the robustness of network architecture for drug discovery

The issue of complexity stands at the center of contemporary drug discovery and development. The central problem in drug development today is attrition of drug candidates identified by the modern molecular target-based discovery approach, due to two related features of complex metabolic networks: their fundamentally unpredictable response to targeted interventions and their "robustness" (tendency to maintain stable function in the face of internal or external perturbations). Complexity and adaptations are, therefore, generally seen as obstacles to drug discovery. Here, the converse proposition is presented-that the complexity and adaptive responses of highly interconnected metabolic networks can be exploited for therapeutic discovery. Unanticipated connectivity relationships may result in "off-target" changes in metabolic fluxes, leading to unexpected therapeutic actions of agents. Exploiting this approach requires that fully assembled living systems (in vivo models) be studied and that informative in vivo biomarkers of the activity of biochemical pathways responsible for disease be available. These biomarkers should be sensitive, predictive of functional endpoints, and have high enough throughput for efficient screening of large numbers of agents. To the extent that such biomarkers unambiguously reflect the activity of pathways that mediate disease or therapeutic response (i.e., are "authentic"), their utility will be increased. Examples are presented of pathway-based screening of approved drugs for unexpected actions. Results support the principle that agents that have one action typically have many actions, including unanticipated actions, reflecting connectivity relationships of complex networks. Pathway-based screening in vivo represents an alternative to the high attrition of the molecular target-based discovery paradigm.

Stimulating the development of mechanism-based, individualized pain therapies

Biomedical science has greatly improved our understanding of pain in recent decades, but few novel molecular entities that address fundamentally new pain mechanisms have entered the clinic, despite dramatically increased pharmaceutical investment. Indeed, virtually all new analgesics approved over the past 25 years are derivatives or reformulations of opioids or aspirin-like drugs, existing drugs given for a new indication or older drugs given by a different route of administration. Here, we discuss factors contributing to this lack of innovation in therapies for pain and advocate public-private partnerships (PPPs) to translate new knowledge into more efficacious and safer treatments.

A critique of the molecular target-based drug discovery paradigm based on principles of metabolic control: advantages of pathway-based discovery

Contemporary drug discovery and development (DDD) is dominated by a molecular target-based paradigm. Molecular targets that are potentially important in disease are physically characterized; chemical entities that interact with these targets are identified by ex vivo high-throughput screening assays, and optimized lead compounds enter testing as drugs. Contrary to highly publicized claims, the ascendance of this approach has in fact resulted in the lowest rate of new drug approvals in a generation. The primary explanation for low rates of new drugs is attrition, or the failure of candidates identified by molecular target-based methods to advance successfully through the DDD process. In this essay, I advance the thesis that this failure was predictable, based on modern principles of metabolic control that have emerged and been applied most forcefully in the field of metabolic engineering. These principles, such as the robustness of flux distributions, address connectivity relationships in complex metabolic networks and make it unlikely a priori that modulating most molecular targets will have predictable, beneficial functional outcomes. These same principles also suggest, however, that unexpected therapeutic actions will be common for agents that have any effect (i.e., that complexity can be exploited therapeutically). A potential operational solution (pathway-based DDD), based on observability rather than predictability, is described, focusing on emergent properties of key metabolic pathways in vivo. Recent examples of pathway-based DDD are described. In summary, the molecular target-based DDD paradigm is built on a naïve and misleading model of biologic control and is not heuristically adequate for advancing the mission of modern therapeutics. New approaches that take account of and are built on principles described by metabolic engineers are needed for the next generation of DDD.

Support vector machines approach for predicting druggable proteins: recent progress in its exploration and investigation of its usefulness

Identification and validation of viable targets is an important first step in drug discovery and new methods, and integrated approaches are continuously explored to improve the discovery rate and exploration of new drug targets. An in silico machine learning method, support vector machines, has been explored as a new method for predicting druggable proteins from amino acid sequence independent of sequence similarity, thereby facilitating the prediction of druggable proteins that exhibit no or low homology to known targets.

Therapeutic targets: progress of their exploration and investigation of their characteristics

Modern drug discovery is primarily based on the search and subsequent testing of drug candidates acting on a preselected therapeutic target. Progress in genomics, protein structure, proteomics, and disease mechanisms has led to a growing interest in and effort for finding new targets and more effective exploration of existing targets. The number of reported targets of marketed and investigational drugs has significantly increased in the past 8 years. There are 1535 targets collected in the therapeutic target database compared with approximately 500 targets reported in a 1996 review. Knowledge of these targets is helpful for molecular dissection of the mechanism of action of drugs and for predicting features that guide new drug design and the search for new targets. This article summarizes the progress of target exploration and investigates the characteristics of the currently explored targets to analyze their sequence, structure, family representation, pathway association, tissue distribution, and genome location features for finding clues useful for searching for new targets. Possible "rules" to guide the search for druggable proteins and the feasibility of using a statistical learning method for predicting druggable proteins directly from their sequences are discussed.

PharmGED: Pharmacogenetic Effect Database

Prediction and elucidation of pharmacogenetic effects is important for facilitating the development of personalized medicines. Knowledge of polymorphism-induced and other types of drug-response variations is needed for facilitating such studies. Although databases of pharmacogenetic knowledge, polymorphism and toxicogenomic information have appeared, some of the relevant data are provided in separate web-pages and in terms of relatively long descriptions quoted from literatures. To facilitate easy and quick assessment of the relevant information, it is helpful to develop databases that provide all of the information related to a pharmacogenetic effect in the same web-page and in brief descriptions. We developed a database, Pharmacogenetic Effect Database (PharmGED), for providing sequence, function, polymorphism, affected drugs and pharmacogenetic effects. PharmGED can be accessed at free of charge for academic use. It currently contains 1825 entries covering 108 disease conditions, 266 distinct proteins, 693 polymorphisms, 414 drugs/ligands cited from 856 references.

Pharmacogenomics and the future of drug therapy

With the completion of the human genome project, many investigators are striving to translate the resulting wealth of new information into new and improved clinical practices. Pharmacogenomics represents one of the most promising of these applications for adult- and pediatric-based therapies. This article provides a historical perspective, but most importantly, uses this background to illustrate important principles of the field. The application of pharmacogenomics to asthma therapy is presented as an example of the current status of pharmacogenomics as it is being applied to an important pediatric health problem. Finally, a discussion of future promises and challenges to the application of pharmacogenomics is presented, including economic and ethical issues.

Progress and problems in the exploration of therapeutic targets

Drugs exert their therapeutic effect by binding and regulating the activity of a particular protein or nucleic acid target. A large number of targets have been explored for drug discovery. Continuous effort has been directed at the search for new targets and more-extensive exploration of existing targets. Knowledge of these targets facilitates the understanding of molecular mechanisms of drugs and the effort required for drug discovery and target searches. Areas of progress, current focuses of research and development and the difficulties in target exploration are reviewed. The characteristics of the currently explored targets and their correlation to the level of difficulty for target exploration are analyzed. From these characteristics, simple rules can be derived for estimating the difficulty level of target exploration. The feasibility of predicting druggable proteins by using simple rules and sequence-derived physicochemical properties is also discussed.

Escherichia coli O antigen typing using DNA microarrays

DNA microarrays were developed for rapid identification of different serogroups of Escherichia coli in a single platform. Oligonucleotides, as well as PCR products from genes in the O antigen gene clusters of E. coli serogroups O7, O104, O111, and O157 were spotted onto glass slides. This was followed by hybridization with labeled long PCR products of the entire O antigen gene clusters of these serogroups. Results demonstrated that microarrays consisting of either oligonucleotides or PCR products generated specific signals for each serogroup. This is the first report describing the development of model DNA microarrays for determining the serogroup of E. coli strains.

Pharmacogenetics in drug development

Over the last two decades, identification of polymorphisms that influence human diseases has begun to have an impact on the provision of medical care. The promise of genetics lies in its ability to provide insights into an individual's susceptibility to disease, the likely nature of the disease and the most appropriate therapy. For much of its history, pharmacogenetics (PGx-the use of genetic information to impact drug choice) has been limited to comparatively simple phenotypes such as plasma drug levels. Progress in genetics technologies has broadened the scope of PGx efficacy and safety studies that can be implemented, impacting on a broad spectrum of drug discovery and development activities. Recent PGx data show the ability of this approach to generate information that can be applied to dose selection, efficacy determination and safety issues. This in turn will lead to significant opportunities to affect both the approach to clinical development and the probability of success--the latter being an important aspect for pharmaceutical companies and for the patients who will benefit from these new medicines.

A Fast Exchange Algorithm for Designing Focused Libraries in Lead Optimization

Combinatorial chemistry is widely used in drug discovery. Once a lead compound has been identified, a series of R-groups and reagents can be selected and combined to generate new potential drugs. The combinatorial nature of this problem leads to chemical libraries containing usually a very large number of virtual compounds, far too large to permit their chemical synthesis. Therefore, one often wants to select a subset of "good" reagents for each R-group of reagents and synthesize all their possible combinations. In this research, one encounters some difficulties. First, the selection of reagents has to be done such that the compounds of the resulting sublibrary simultaneously optimize a series of chemical properties. For each compound, a desirability index, a concept proposed by Harrington,(20) is used to summarize those properties in one fitness value. Then a loss function is used as objective criteria to globally quantify the quality of a sublibrary. Second, there are a huge number of possible sublibraries, and the solutions space has to be explored as fast as possible. The WEALD algorithm proposed in this paper starts with a random solution and iterates by applying exchanges, a simple method proposed by Fedorov(13) and often used in the generation of optimal designs. Those exchanges are guided by a weighting of the reagents adapted recursively as the solutions space is explored. The algorithm is applied on a real database and reveals to converge rapidly. It is compared to results given by two other algorithms presented in the combinatorial chemistry literature: the Ultrafast algorithm of D. Agrafiotis and V. Lobanov and the Piccolo algorithm of W. Zheng et al.

Pharmacogenetics and drug development: the path to safer and more effective drugs

Pharmacogenetics provides opportunities for informed decision-making along the pharmaceutical pipeline. There is a growing literature of retrospective studies of marketed medicines that describe efficacy or safety on the basis of patient genotypes. These studies emphasize the potential prospective use of genome information to enhance success in finding new medicines. An example of a prospective efficacy pharmacogenetic Phase-IIA proof-of-concept study is described. Inserting a rapidly performed efficacy pharmacogenetic step after initial clinical data are obtained can provide confidence for a commitment to full drug development. The rapid identification of adverse events during and after drug development using genomic mapping tools is also reviewed.

Molecular cloning, expression, and sequence analysis of GPRC6A, a novel family C G-protein-coupled receptor

By similarity searching of the human genome sequence using known family C G-protein-coupled receptors (GPCRs) as query sequences, we have identified a putative novel human gene product of unknown function (located on chromosome band 6q22.31). The transcript, entitled GPRC6A (isoform 1), was cloned from a human kidney cDNA (DNA complementary to RNA) library and shown to encode a protein of 926 amino acids (aa). Protein sequence analysis revealed the presence of a seven-transmembrane (7TM) domain and an unusually long amino-terminal domain (ATD) of 590 amino acids. These traits, along with a significant homology to the human calcium-sensing receptor (CaR, 34% aa sequence identity), the taste receptor 1 (T1R1, 28%), and the metabotropic glutamate receptor 1 (mGluR1, 24%), places GPRC6A in family C of the GPCRs. Interestingly, GPRC6A bears the highest resemblance with an odorant goldfish 5.24 receptor (45%) which suggests that GPRC6A is the human orthologue of this receptor. GPRC6A is widely expressed in brain and peripheral tissues with highest levels in kidney, skeletal muscle, testis, and leucocytes. All three isoforms are expressed in mammalian cells, but are poorly expressed on the cell surface. In this work, we report the existence of two additional GPRC6A isoforms (2 and 3) carrying in-frame deletions in the ATD. Except for the kidney, where isoforms 1 and 2 appear equally expressed, isoforms 2 and 3 are generally less abundant than isoform 1. Analysis of the intron-exon composition of the GPRC6A gene confirms that isoforms 2 and 3 are naturally occurring splice variants.

[Pharmacogenetics and pharmacogenomics of cancers]

Sequencing the human genome brings new tools for the individualisation of cancer chemotherapy, firstly thanks to the identification of polymorphisms of genes involved in anticancer drug metabolism or activity (Pharmacogenetics), and secondly thanks to the determination of tumour gene expression profiles and their relationship to chemosensitivity and chemoresistance (Pharmacogenomics). A few functional polymorphisms have been known for a long time (thiopurine methyltransferase, glutathion S-transferases), but several new ones have been identified recently, at the level of the genes encoding drug targets (thymidylate synthase), at the level of DNA repair enzymes (XPD) or at the level of transport proteins (MDR1). On the other hand, the research of correlations between gene expression profiles and chemosensitivity has been performed on the in vitro models of the National Cancer Institute and may allow crucial improvements in the identification of patients who would best take advantage of a specific chemotherapy. Clinical trials, first on a retrospective basis, then on a prospective one, are implemented to validate this approach.

N/A

N/A

A brief history of novel drug discovery technologies

Laypersons, researchers and clinicians alike speak of the biotechnology revolution with excitement. Media coverage of new breakthroughs in medicine often have the public and the investment community on the edge of their seats, eager for the next blockbuster drug to cure everything from high cholesterol levels to cancer. In this perspective, we examine some of the more popularized and influential new technologies in drug discovery and assess their relative impact on the actual attainment of new therapeutics.

Absolute quantification of the G protein-coupled receptor rhodopsin by LC/MS/MS using proteolysis product peptides and synthetic peptide standards

Methods for the absolute quantification of a membrane protein are described using isotopically labeled or unlabeled synthetic peptides as standards. Synthetic peptides are designed to mimic peptides that are cleaved from target analyte proteins by proteolytic or chemical digestion, and the peptides selected serve as standards for quantification by LC/MS/MS on a triple quadrupole mass spectrometer. The technique is complementary to relative quantification techniques in widespread use by providing absolute quantitation of selected targets with greater sensitivity, dynamic range, and precision. Proteins that are found to be of interest by global proteome searches can be selected as targets for quantitation by the present method. This method has a much shorter analytical cycle time (minutes versus hours for the global proteome experiments), making it well suited for high-throughput environments. The present approach using synthetic peptides as standards, in conjunction with proteolytic or chemical cleavage of target proteins, allows mass spectrometry to be used as a highly selective detector for providing absolute quantification of proteins for which no standards are available. We demonstrate that quantification is simple and reliable for the integral membrane protein rhodopsin with reasonable recoveries for replicate experiments using low-micromolar solutions of rhodopsin from rod outer segments.

Utilizing functional genomics to identify new pain treatments : the example of nociceptin

Nociceptin/orphanin FQ (noc/oFQ) is the first novel bioactive substance to have been discovered by the implementation of a functional genomics/reverse pharmacology approach. The neuropeptide was indeed identified in brain extracts as the natural ligand of a previously cloned orphan G protein-coupled receptor, the opioid receptor-like 1 (ORL1) receptor. Since its discovery in 1995, noc/oFQ has been the subject of intensive study to establish its role in normal brain function and its possible involvement in neurophysiopathology. Although the neuropeptide, an inhibitor of neuronal activity, has been found to have a wide spectrum of pharmacological effects in vivo, none has been as intensively investigated as its action on nociception and nociceptive processing. There is now substantial evidence that noc/oFQ has a modulatory role in nociception. However, dependent on the dose and site of injection, and possibly the animal's genetic background and even psychological status, the peptide has been variously reported to cause allodynia, hyperalgesia, analgesia, and even pain, in rodents. Overall, noc/oFQ tends to facilitate pain when administered supraspinally, and to inhibit it when administered spinally. These opposing effects beg the obvious, yet still unanswered, question as to what would be the net effect on nociception of an ORL1 receptor ligand, agonist or antagonist, able to target supraspinal and spinal sites simultaneously. Owing to the research effort of several drug companies, such ligands, i.e. nonpeptidic, brain-penetrating agonists and antagonists, have recently been produced whose systematic screening in animal models of acute and inflammatory pain may help validate the ORL1 receptor as the target for novel, non-opioid analgesics.

Influence of ageing, heat shock treatment and in vivo total antioxidant status on gene-expression profile and protein synthesis in human peripheral lymphocytes

Ageing results in a progressive, intrinsic and generalised imbalance of the control of regulatory systems. A key manifestation of this complex biological process includes the attenuation of the universal stress response. Here we provide the first global assessment of the ageing process as it affects the heat shock response, utilising human peripheral lymphocytes and cDNA microarray analysis. The genomic approach employed in our preliminary study was supplemented with a proteomic approach. In addition, the current study correlates the in vivo total antioxidant status with the age-related differential gene expression as well as the translational kinetics of heat shock proteins (hsps). Most of the genes encoding stress response proteins on the 4224 element microarray used in this study were significantly elevated after heat shock treatment of lymphocytes obtained from both young and old individuals albeit to a greater extent in the young. Cell signaling and signal transduction genes as well as some oxidoreductases showed varied response. Results from translational kinetics of induction of major hsps, from 0 to 24 h recovery period were broadly consistent with the differential expression of HSC 70 and HSP 40 genes. Total antioxidant levels in plasma from old individuals were found to be significantly lower by comparison with young, in agreement with the widely acknowledged role of oxidant homeostasis in the ageing process.

Optimization and evaluation of T7 based RNA linear amplification protocols for cDNA microarray analysis

BACKGROUND

T7 based linear amplification of RNA is used to obtain sufficient antisense RNA for microarray expression profiling. We optimized and systematically evaluated the fidelity and reproducibility of different amplification protocols using total RNA obtained from primary human breast carcinomas and high-density cDNA microarrays.

RESULTS

Using an optimized protocol, the average correlation coefficient of gene expression of 11,123 cDNA clones between amplified and unamplified samples is 0.82 (0.85 when a virtual array was created using repeatedly amplified samples to minimize experimental variation). Less than 4% of genes show changes in expression level by 2-fold or greater after amplification compared to unamplified samples. Most changes due to amplification are not systematic both within one tumor sample and between different tumors. Amplification appears to dampen the variation of gene expression for some genes when compared to unamplified poly(A)+ RNA. The reproducibility between repeatedly amplified samples is 0.97 when performed on the same day, but drops to 0.90 when performed weeks apart. The fidelity and reproducibility of amplification is not affected by decreasing the amount of input total RNA in the 0.3-3 micrograms range. Adding template-switching primer, DNA ligase, or column purification of double-stranded cDNA does not improve the fidelity of amplification. The correlation coefficient between amplified and unamplified samples is higher when total RNA is used as template for both experimental and reference RNA amplification.

CONCLUSION

T7 based linear amplification reproducibly generates amplified RNA that closely approximates original sample for gene expression profiling using cDNA microarrays.

Barriers to Alzheimer disease drug discovery and development in the biotechnology industry

The major barrier to Alzheimer disease (AD) drug discovery and development in the biotechnology industry is scale. Most biotechnology companies do not have the personnel or expertise to carry a drug from the bench to the market. Much effort in the industry has been directed toward the elucidation of molecular mechanisms of AD and the identification of new targets. Advances in biotechnology have generated new insights into disease mechanisms, increased the number of lead compounds, and accelerated biologic screening. The majority of costs associated with drug development are in clinical testing and development activities, many of which are driven by regulatory issues. For most biotechnology companies, the costs of such trials and the infrastructure necessary to support them are prohibitive. Another significant barrier is the definition of therapeutic benefit for AD drugs; Food and Drug Administration (FDA) precedent has established that a drug must show superiority to placebo on a performance-based test of cognition and a measure of global clinical function. This restrictive definition is biased toward drugs that enhance performance on memory-based tests. Newer AD drugs are targeted toward slowing disease progression; however, there is currently no accepted definition of what constitutes efficacy in disease progression. Despite these obstacles, the biotechnology industry has much to offer AD drug discovery and development. Biotechnology firms have already developed essential technology for AD drug development and will continue to do so. Biotechnology companies can move more quickly; of course, the trick is to move quickly in the right direction. Speed may offset some of the problems associated with lack of scale. Additionally, biotechnology companies can afford to address markets that may be too restricted for larger pharmaceutical companies. This advantage will have increasing importance, as therapies are developed to address subtypes of AD.

Drug receptor identification from multiple tissues using cellular-derived mRNA display libraries

The use of display technologies to identify small molecule receptors from proteome libraries would provide a significant advantage in drug discovery. We have used mRNA display to select, based on affinity, proteins that bind to a drug of interest. A library of mRNA-protein fusion molecules was constructed from human liver, kidney, and bone marrow transcripts and selected using an immobilized FK506-biotin conjugate. Three rounds of selection produced full-length FKBP12 (FK506 binding protein 12 kDa) as the dominant clone. An analogous method was also used to map the minimal drug binding domain within FKBP12. Using this approach, it is anticipated that mRNA display could eventually play a key role in the discovery and characterization of new drug receptor interactions.