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• W2059943395 abstract "Drug discovery is fundamentally focused on increasing the number and novelty of medicines, while containing the associated costs. High costs impact on healthcare delivery and the range of diseases for which medicines become available. Pace has been increased via combinatorial chemistry, genomics knowledge, proteomics technologies, new target validation methodologies, miniaturization, and high throughput screening (HTS). The integration of cytometry-based platforms has offered increasingly sophisticated levels of multiplexing (1). Cytomics offers information on the complexity, state, and behavior of heterogeneous cellular systems—reaching into the time domain of specific processes and the spatial changes in participating molecules and structures. Exploiting cytomic approaches and evolving parameters for use in computational biology could support both the screening and development of molecular therapeutics. The role of cytomics in drug discovery and development is best appreciated with respect to the problems, drivers and challenges of the healthcare sector. In this short “perspective” the aim is to profile some of these issues for the wider cytometry community. The pharmaceutical industry's difficulty in finding new molecular entities (NMEs) is well documented, with high candidate attrition rates in the discovery through to development phases. Only 25% of high-expectation lead compounds that enter clinical trials ever reach the market, and even fewer are blockbusters. Over the last decade or so, the response of the top-end of the industry has been a massive investment in high throughput methods (genomics, HTS, and combinatorial chemistry). Clear outcomes of the adoption of high throughput methods have been the continued development of flexible discovery platforms and the unparalleled production of data mountains. Within the area of cell-based research and technologies, the extended cytometry community has core competences to meet the cytomic challenges. Efficient identification and optimization of potent lead molecules is still the highest and riskiest hurdle in current drug discovery and development. A study of global pharmaceutical performance for the critical period of 1994–2003 has revealed that the rise seen in global sales was matched by the rise in global R&D expenditure, with no improvement in development time (2). Furthermore, during the same period the output of NMEs, including products of biotechnology, fell by 30% (2). The ensuing pressures are against a backdrop of a tacit acceptance that the future will in part embrace individualized medicine, demanding niche therapeutics for specific disease areas. In this future, key new technological solutions are also envisioned, including stem-cell–based regenerative medicine, pharmacogenomic profiling for prediction and monitoring of clinical response, and miniaturized detection for point-of-care diagnostics providing laboratory quality tests in real time. Cytomics maps both to current cell-based assays in the HTS/high content screening drive and to future microtechnologies particularly biochips for cell-based analyses (3). Drug discovery is a race—how quickly can you match compound to target. Drug development is high maintenance. The identification of potential targets is relatively straightforward while the validation of a target, as a suitable point for pharmacological intervention in a given disease, remains difficult and increasingly reliant on functional genomics. A HTS program may produce hundreds of lead compounds, driving the need to use low-maintenance animal cytome models (e.g. zebra fish embryos) for preclinical testing. Medicinal chemistry takes leads through iterative rounds of optimization (eg to improve solubility) while issues of potency, ADME/tox (absorption, distribution, metabolism, excretion and toxicity), pharmacodynamics, administration route, and potential drug interactions also may be assessed. The mechanism of action of the drug may be established and quantitative structure/activity studies to predict pharmacokinetic and pharmacodynamic features from structural/chemical features undertaken. Again cytomics approaches can underpin a range of complementary studies including predictive cellular modeling of micropharmacokinetics and target interactions (4), the use of transgenics to develop informative primary cell gene-knockout systems (5, 6), and methodologies for tracking routes for drug resistance (7). The cell cycle is an attractive and intellectually elegant target for anticancer agents with intensive research in the cytometry and drug development communities (8). The recognition of apparently legitimate drug targets such as the cyclin dependent kinases (CDKs) has prompted elegant approaches to the development of specific inhibitors (9). However, phenotypic characterization of genetically modified mice lacking cyclins and CDKs have challenged many previous assumptions related to cell cycle control in mammals—most of the G1 cyclins and CDKs turning out to be dispensable in most cell types (10). Investment in genomics and proteomics resources have led to an increased drive to deploy even more sophisticated approaches to target selection and drug development with variable success. For example in the oncotherapeutics arena, imatinib mesylate (Gleevec) of Novartis' (Basel, Switzerland) has been hailed as a breakthrough in cancer treatment while other compounds that specifically target protein kinases have found success more elusive (11). The industry is attempting to address failure due to toxicity by pushing the “ADME/Tox” testing to the first stages of the discovery process. The use of high content primary screens where multiple parameters are measured in whole cells may enable some indicator parameters of toxicity to become apparent earlier in that process. This is a current center of gravity for cytomics. Moving analyses to cytomics-based systems can generate massively diverse datasets from images to quantitative analyses relating to the cell (eg location proteomics;12) or the embedded information in reporter measurements (eg fluorescence lifetime deconvolution;13). Moreover, the chemical “diversity space” for lead discovery is also expanding. Data must be captured, stored, annotated, and analyzed—presenting a formidable challenge when expanded by the results of time-based studies. Importantly, the unsupervized re-mining of rich datasets is likely to be an important resource for serendipitous discovery. Each of the various types of data handled in the drug discovery process poses its own specific problems for integration into information systems and decision-making processes. HTS and HCS approaches for in vitro cell-based assays where time is the quality parameter demand unique solutions for rapid and automated image analysis and encoding routines (6). Timelapse microscopy tracking of sequential events on a cell-by-cell basis can provide access to normally occult behavior patterns. The problem is how to mine that information through cellular bioinformatics tools. Here cytomics is set to grapple with the complexity of multiple asynchronous lineages in cell-based assays. A cellular bioinformatics challenge is for the behavior of both a drug-exposed progenitor and the descending line of offspring be tracked using cytometric techniques and encode in silico to reveal the full time-integrated pharmacodynamic response (e.g. changes in intergeneration cell division time or cell death). Databases containing parameterized lineages for exemplar agents could act a shared resource evolved by the cyotmics community not dissimilar to the US NCI developmental therapeutics database. In the end, the drug discovery and development process is dependent on informed decision making. Cytomics needs to offer an increase in the quality and pertinence of information for that decision-making process. At the trial stage, regulatory bodies need to be convinced of the worthiness of a new medicine within a continued tightening of performance requirements and trials directives. Bridging the discovery/development process and the clinical trials phase is a real challenge for cytomics. This is attainable—given for example the paradigm established by immunophenotyping in leukemia/lymphoma trials. We should expect a major contribution from the enormous virtual resource within the academic research and cytometry community towards the drug discovery process. Big pharma has recently watched a rise in FDA approvals for NMEs developed by the expanding biotech sector. Accordingly, in-licensing remains a popular strategic option in medicine development, with the recognition that licensed compounds are often developed from novel concepts and more likely to be outside the pharmaceutical companies' core research competencies (14). The academic cytometry community can look forward to increasing levels of partnership with small- and medium-sized biotechnology companies as they seek to access specific areas of expertise. Cytomics wields a double-edged sword for drug discovery and development. On one hand, the increasing dynamic to push critical screening steps to as early a stage in the drug discovery process as possible (“fast to fail”) has led to the use of the cell and higher level cytomes (eg zebra fish embryos) as informative screening environments (15). Here, as always, the important “–omic” is economics. It is likely that the primary method of getting data will not be the effective source of competitive advantage. On the other, it seems likely that we have already approached a bottle-neck in converting data into information with predictive capacity Easy access to meta data (information) via a common portal (eg genomic data is accessible by the The National Center for Biotechnology Information web site) is clearly needed. Generation of a common language through which data from acquisition laboratories can be transported and translated at other sites (eg using a Systems Biology Markup Language) is vital. Placing cytomics into a cellular systems biology context will offer real advantage to drug discovery." @default.
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• W2059943395 title "Cytomics and drug development" @default.
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