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PubMed2004

Nonredundant mass spectrometry: a strategy to integrate mass spectrometry acquisition and analysis.

Protein identification using automated data-dependent tandem mass spectrometry (MS/MS) is now a standard procedure. However, in many cases data-dependent acquisition becomes redundant acquisition as many different peptides from the same protein are fragmented, whilst only a few are needed for unambiguous identification. To increase the quality of information but decrease the amount of information, a nonredundant MS (nrMS) strategy has been developed. With nrMS, data analysis is an integral part of the overall MS acquisition and analysis, and not an endpoint as typically performed. In this nrMS workflow a matrix assisted laser desorption/ionization-time of flight-time of flight (MALDI-TOF/TOF) instrument is used. MS and restricted MS/MS data are searched and identified proteins are used to generate an "exclusion list", after in silico digestion. Peptide fragmentation is then restricted to only the most intense ions not present in the exclusion list. This process is repeated until all peaks are accounted for or the sample is consumed. Compared to nanoLC-MS/MS, nrMS yielded similar results for the analysis of six pooled two-dimensional electrophoresis (2-DE) spots. In comparison to standard data-dependent MALDI-MS/MS for sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) gel band analysis, nrMS dramatically increased the number of identified proteins. It was also found that this new workflow significantly increased sequence coverage by identifying unexpected peptides, which can result from post-translational modifications.

Algorithms↗

PubMed2026

Digital and computational morphology in hematology: current platforms, clinical evidence, and future requirements.

INTRODUCTION: Morphologic examination of peripheral blood and bone marrow remains central to the diagnosis and classification of hematologic disorders. Conventional optical microscopy, however, is labor-intensive, dependent on operator expertise, and affected by interobserver variability. Digital morphology has developed from automated image acquisition and cell pre-classification into a broader field that includes whole-slide imaging, remote review, quantitative morphometry, and artificial intelligence-based analysis. CONTENT: This review examines current applications of digital morphology in peripheral blood, bone marrow aspirates, malaria detection, and body-fluid analysis. Commercial platforms are evaluated with particular attention to the distinction between raw automated pre-classification, expert digital post-classification, and comparison with independent optical microscopy. Digital systems generally perform well for common mature leukocyte populations but remain less reliable for rare or diagnostically critical cells, including blasts, abnormal lymphoid cells, plasma cells, and intermediate maturation stages. Research systems increasingly extend analysis from individual-cell classification to whole-slide, specimen-level, and patient-level assessment. SUMMARY: Digital morphology can improve standardization, image traceability, remote consultation, education, proficiency testing, quality assurance, and selected aspects of laboratory workflow. Its clinical value depends on appropriate validation, transparent reporting of reference methods, recognition of algorithm-specific failure modes, and clearly defined criteria for expert review and conventional microscopy. Human expertise remains essential not only for validating results but also for adapting cell taxonomies and interpretive rules to evolving classifications of hematologic diseases. OUTLOOK: Future progress will require representative multicenter datasets, harmonized morphologic terminology, external validation, interoperability with laboratory information systems, and continuous monitoring after software or hardware updates. Integration of morphology with quantitative hematology, flow cytometry, cytogenetics, genomics, and clinical data may support more comprehensive computational diagnosis. Digital platforms may also broaden access to specialist expertise, training, and quality programs in resource-limited institutions and regions, provided that infrastructure, governance, and professional competency are adequately supported.

artificial intelligence↗

PubMed2005

Lack of effect of shorter turnaround time of microbiological procedures on clinical outcomes: a randomised controlled trial among hospitalised patients in the Netherlands.

Shortening the turnaround time of microbiological procedures was associated with an improved clinical outcome in two studies performed in the USA. To study the clinical impact of a shortened turnaround time in a northwest European setting in which an automated system was used for bacterial identification and susceptibility testing, a single-blind, prospective, randomised controlled trial was conducted in a hospital in the Netherlands. All hospitalised patients with a bacterial infection confirmed by culture were randomly assigned to a control (conventional) group or an intervention (rapid) group. Overnight methods were used for identification and susceptibility testing in the control group, while the Vitek 2 system (bioMerieux, Marcy l'Etoile, France) was used in the rapid group. In each of three consecutive study periods, accelerating factors were added progressively to the laboratory workflow of the rapid group to increase same-day reporting, whereas methods remained identical in the conventional group. The turnaround time of the microbiological cycle using the Vitek 2 system as compared to conventional methods was studied and the clinical impact of a shortened turnaround time assessed in terms of mortality, morbidity, and cost. For the rapid groups, the turnaround time was significantly shorter for oral reporting of final susceptibility results in all three study periods and for reporting on paper in the third study period. There was no significant difference between groups in any of the clinical impact variables. Vitek 2 results were available for reporting significantly earlier as compared to conventional testing. For the overall patient group in our hospital setting, however, this had no clinical impact.

Aged↗

PubMed2004

A web services choreography scenario for interoperating bioinformatics applications.

BACKGROUND: Very often genome-wide data analysis requires the interoperation of multiple databases and analytic tools. A large number of genome databases and bioinformatics applications are available through the web, but it is difficult to automate interoperation because: 1) the platforms on which the applications run are heterogeneous, 2) their web interface is not machine-friendly, 3) they use a non-standard format for data input and output, 4) they do not exploit standards to define application interface and message exchange, and 5) existing protocols for remote messaging are often not firewall-friendly. To overcome these issues, web services have emerged as a standard XML-based model for message exchange between heterogeneous applications. Web services engines have been developed to manage the configuration and execution of a web services workflow. RESULTS: To demonstrate the benefit of using web services over traditional web interfaces, we compare the two implementations of HAPI, a gene expression analysis utility developed by the University of California San Diego (UCSD) that allows visual characterization of groups or clusters of genes based on the biomedical literature. This utility takes a set of microarray spot IDs as input and outputs a hierarchy of MeSH Keywords that correlates to the input and is grouped by Medical Subject Heading (MeSH) category. While the HTML output is easy for humans to visualize, it is difficult for computer applications to interpret semantically. To facilitate the capability of machine processing, we have created a workflow of three web services that replicates the HAPI functionality. These web services use document-style messages, which means that messages are encoded in an XML-based format. We compared three approaches to the implementation of an XML-based workflow: a hard coded Java application, Collaxa BPEL Server and Taverna Workbench. The Java program functions as a web services engine and interoperates with these web services using a web services choreography language (BPEL4WS). CONCLUSION: While it is relatively straightforward to implement and publish web services, the use of web services choreography engines is still in its infancy. However, industry-wide support and push for web services standards is quickly increasing the chance of success in using web services to unify heterogeneous bioinformatics applications. Due to the immaturity of currently available web services engines, it is still most practical to implement a simple, ad-hoc XML-based workflow by hard coding the workflow as a Java application. For advanced web service users the Collaxa BPEL engine facilitates a configuration and management environment that can fully handle XML-based workflow.

Computational Biology↗

PubMed2005

Human and system errors, using adaptive turnaround documents to capture data in a busy practice.

Capturing coded clinical data for clinical decision support can improve care, but cost and disruption of clinic workflow present barriers to implementation. Previous work has shown that tailored, scannable paper forms (adaptive turnaround documents, ATDs) can achieve the benefits of computer-based clinical decision support at low cost and minimal disruption of workflow. ATDs are highly accurate under controlled circumstances, but accuracy in the setting of busy clinics with untrained physician users is untested. We recently developed and implemented such a system and studied rates of errors attributable to physician users and errors in the system. Prompts were used in 63% of encounters. Errors resulting from incorrectly marking forms occurred in 1.8% of prompts. System errors occurred in 7.2% of prompts. Most system errors were failures to capture data and may represent human errors in the scanning process. ATDs are an effective way to collect coded data from physicians. Further automation of the scanning process may reduce system errors.

Ambulatory Care Information Systems↗

PubMed2026

In silico analysis and comparison of the metabolic capabilities of different organisms by reducing metabolic complexity.

BACKGROUND: Understanding how metabolic capabilities diverge across microbial species is essential for deciphering community function, ecological interactions, and the design of synthetic microbiomes. Despite shared core pathways, microbial phenotypes can differ markedly due to evolutionary adaptations and metabolic specialization. Genome-scale metabolic models (GEMs) provide a systems-level framework to explore these differences; however, their complexity hinders direct comparison. RESULTS: We introduce NIS (Neidhardt-Ingraham-Schaechter), a computational workflow that integrates the redGEM, lumpGEM, and redGEMX algorithms to systematically reduce genome-scale models into biologically interpretable modules. This approach enables direct, quantitative comparison of fueling pathways, biomass biosynthetic routes, and environmental exchange processes while retaining essential metabolic information. We first demonstrate the utility of NIS by analyzing Escherichia coli and Saccharomyces cerevisiae, which revealed both conserved and divergent strategies in central metabolism, biosynthetic cost, and substrate utilization. We then applied NIS to the core honeybee gut microbiome, uncovering distinct metabolic traits, functional redundancy, and complementarity that help explain auxotrophy, cross-feeding interactions, and microbial coexistence. CONCLUSIONS: NIS provides an automated, scalable, and reproducible framework for dissecting microbial metabolic networks beyond gene content or taxonomy. By linking metabolism to ecological function, NIS offers new opportunities to interpret microbial community dynamics and to support the rational design of microbiomes in health, agriculture, and environmental applications. Video Abstract.

Metabolic Networks and Pathways↗

PubMed1978

Semi-automatic data processing in clinical chemistry.

A semi-automatic system for data processing in the Clinical Chemistry Department of the School of Pathology, Johannesburg, is outlined. Careful planning of workflow in the laboratory can provide a better interface of laboratory results with a manual cumulative reporting system in which technologists and clerks co-ordinate their activities. The system used provides a less tedious and less error-prone process via automatic printing facilities. This system achieves improved patient care in terms of quality control (data checks) and cumulative reporting of results. Facility for accounts and administration purpose are also incorporated. In our experience, semi-automatic data processing can provide a solution to the clerical problems generated by the introduction of automatic testing equipment. Hardware costs are minimal in comparison to fully automated systems, which utilize on-line or off-line computers.

Chemistry, Clinical↗

PubMed2026

Artificial Intelligence for Natural Products Discovery and Development.

Natural products (NPs) remain a cornerstone of modern drug discovery, offering stereochemical complexity and diverse bioactivities that precisely modulate therapeutic targets, refined through billions of years of evolution. However, their research has long been hindered by inefficient, empirical workflows, high resource consumption, structural complexity, and the "multicomponent, multi-target" nature of their mechanisms. The exponential growth of genomic, metabolomic, and spectral data has overwhelmed conventional analytical methods, exposing critical bottlenecks in handling high-dimensional, heterogeneous datasets that exceed human interpretive capacity. Artificial intelligence (AI) is emerging as a transformative paradigm to address these challenges, integrating multi-omics and chemical data to shift NP research from fragmented empiricism toward mechanism-driven, precision-oriented development. By leveraging deep learning architectures- including graph neural networks, Transformers, and diffusion-based generative models-AI enables systematic decoding of NP biosynthesis, automated structure elucidation, rational target identification, knowledge extraction from vast unstructured scientific literature, and de novo molecular design. This review comprehensively surveys recent advances in AI applications across the full NP discovery and development pipeline, encompassing genome mining, structure-based and ligand-based virtual screening, multimodal structural characterization, lead optimization, and biosynthetic pathway engineering. We further examine the emerging roles of protein-centric, molecule- centric, and multimodal foundation models, as well as large language models, in bridging genotype-to-chemotype gaps and unlocking unstructured scientific knowledge. Finally, we discuss critical challenges including data scarcity, representational limitations for complex stereochemistry, physical plausibility in generative models, and the urgent need for experimental validation, while outlining future directions toward autonomous experimentation, closed-loop optimization, and human-AI collaborative discovery.

Artificial intelligence↗

PubMed1993

Electronic medical record systems as a basis for computer-based patient records.

In summation, some document imaging systems offer the capability to form what may be called electronic medical record (EMR) systems. These systems are adaptations of current paper-based record management systems into the digital environment, but they offer far more capabilities than strictly archival, historical functions. Some of the capabilities that create EMR systems will also be necessary for development of CPRs, these capabilities are listed as follows: mass storage and image management; direct capture, storage, and retrieval of digital information (native format); large volume, high-speed, client-server networks; multi-media information management; high-power, flexible database tools; workflow process software; flexible, full function security; user customizable features; and alerts and reminders. Selection and implementation of document imaging systems should, at present, be undertaken with great care to insure that the platform may be utilized to form an electronic medical record with a clear migration path to the CPR.

Database Management Systems↗

PubMed2026

The Gyrolab platform for immunogenicity assessment and biotherapeutic and biomarker analysis: technical advances and bioanalytical applications.

Gyrolab is an automated ligand‑binding assay platform designed for the quantification of proteins with high sensitivity and broad dynamic range. Its low minimum required dilution and microfluidic disc format enable efficient sample processing while maintaining assay robustness, making it a valuable tool across drug‑development stages. This review summarizes published applications of Gyrolab for pharmacokinetic and toxicokinetic analysis and immunogenicity assessment through anti‑drug antibody detection. In addition, the platform's high-volume assay discs have facilitated its use in biomarker studies, allowing quantification of low‑abundance analytes. Representative examples from the literature are presented together with key assay elements, including analytes, matrices, dynamic ranges, and critical reagents. Because assay replicates remain an important consideration for Gyrolab workflows, recent publications addressing singlet versus duplicate strategies and their impact on data interpretation are also discussed. Finally, we provide real analytical data visualized using a three‑dimensional Gyrolab viewer to illustrate variability in duplicate measurements and to highlight future opportunities for improving assay reliability.

Humans↗

PubMed2025

Multi-criteria decision making and its application to in silico discovery of vaccine candidates for Toxoplasma gondii.

Vaccine discovery against eukaryotic parasites is not trivial and few exist. Reverse vaccinology is an in silico vaccine discovery approach, designed to identify vaccine candidates from the thousands of protein sequences encoded by a target genome. Previously, we produced the Vacceed bioinformatics pipeline for identification of parasite membrane and excreted/secreted proteins that were likely be exposed to the hosts immune system. More recently, we improved upon machine learning as the final decision-making process to identify parasite proteins that induce a protective response in an animal model. Subsequently, we combined Vacceed with metrics on B and T cell epitope types to produce a new in silico discovery workflow. In this study we extend this in silico workflow to the developability of proteins as vaccines by the incorporation of metrics on the physicochemical properties of proteins. To demonstrate this process, every Toxoplasma gondii protein was ranked in its capacity to provide exposure to the immune system (Vacceed exposure score), presence of epitopes and solubility characteristics by several multicriteria decision making (MCDM) tools (such as TOPSIS, VIKOR and MABAC). A consensus rank was subsequently generated from the results of these tools using a variety of aggregate ranking methods. Levels of uncertainty in the aggregate protein rankings was assessed by conformal interval prediction in association with a machine learning model. Several of the top ranked proteins identified by this approach were novel, uncharacterized membrane transporters or proteins associated with RNA metabolism. In conclusion, MCDM automated the decision making using well known algorithms while conformal prediction intervals varied significantly across the 8000+ proteins of T. gondii. Highly ranked proteins (e.g. the top 100) typically generated low prediction intervals, providing high levels of confidence in their ranks.

Toxoplasma↗

PubMed2005

Development of functional requirements for electronic health communication: preliminary results from the ELIN project.

BACKGROUND: User participation is important for developing a functional requirements specification for electronic communication. General practitioners and practising specialists, however, often work in small practices without the resources to develop and present their requirements. It was necessary to find a method that could engage practising doctors in order to promote their needs related to electronic communication. MATERIALS AND METHODS: Qualitative research methods were used, starting a process to develop and study documents and collect data from meetings in project groups. Triangulation was used, in that the participants were organised into a panel of experts, a user group, a supplier group and an editorial committee. RESULTS: The panel of experts created a list of functional requirements for electronic communication in health care, consisting of 197 requirements, in addition to 67 requirements selected from an existing Norwegian standard for electronic patient records (EPRs). Elimination of paper copies sent in parallel with electronic messages, optimal workflow, a common electronic 'envelope' with directory services for units and end-users, and defined requirements for content with the possibility of decision support were the most important requirements. CONCLUSIONS: The results indicate that we have found a method of developing functional requirements which provides valid results both for practising doctors and for suppliers of EPR systems.

Attitude of Health Personnel↗

PubMed2006

Stepwise strategies in analysing haematuria and leukocyturia in screening.

The aim of the present work was to compare in a supposed healthy population of 680 subjects several algorithms for positive selection of urine samples requiring microscopic examination for erythrocytes and leukocytes after screening by automated test-strip measurement and particle counting on a Sysmex UF-50 flow cytometer. Four strategies have been formulated and the sensitivity, specificity, positive predictive value, negative predictive value, false positive rate, false negative rate, and microscopic review rate were measured. The strategy combining test strip analysis and automated counting on all samples, followed by microscopic examination of only discordant samples gave the best results. When the two methods of haematuria screening were in agreement (91% of samples), the false negative rate for microscopy was 1.1%, with a false positive rate of 0.8%, sensitivity of 66% and specificity of 99%, and the results are acceptable without any other examination. When the two methods of haematuria screening were discrepant, visual microscopic analysis was necessary to obtain definitive results. For leukocyturia screening, 80% of results were in agreement by test strip and automatic sediment urinalysis, with only ten results considered as false negatives (1.8%) and four as false positives (0.7%). Agreement was good and the other criteria were good (sensitivity 79%, specificity 99%). On conflicting samples, there was no agreement between methods and microscopic analysis was essential. The benefit of such an algorithm would be optimisation of the workflow without any loss of sensitivity and specificity at the expense of a two-fold increase in cost.

Adult↗

PubMed2004

A computerized clinical decision support system as a means of implementing depression guidelines.

The authors describe the history and current use of computerized systems for implementing treatment guidelines in general medicine as well as the development, testing, and early use of a computerized decision support system for depression treatment among "real-world" clinical settings in Texas. In 1999 health care experts from Europe and the United States met to confront the well-documented challenges of implementing treatment guidelines and to identify strategies for improvement. They suggested the integration of guidelines into computer systems that is incorporated into clinical workflow. Several studies have demonstrated improvements in physicians' adherence to guidelines when such guidelines are provided in a computerized format. Although computerized decision support systems are being used in many areas of medicine and have demonstrated improved patient outcomes, their use in psychiatric illness is limited. The authors designed and developed a computerized decision support system for the treatment of major depressive disorder by using evidence-based guidelines, transferring the knowledge gained from the Texas Medication Algorithm Project (TMAP). This computerized decision support system (CompTMAP) provides support in diagnosis, treatment, follow-up, and preventive care and can be incorporated into the clinical setting. CompTMAP has gone through extensive testing to ensure accuracy and reliability. Physician surveys have indicated a positive response to CompTMAP, although the sample was insufficient for statistical testing. CompTMAP is part of a new era of comprehensive computerized decision support systems that take advantage of advances in automation and provide more complete clinical support to physicians in clinical practice.

Algorithms↗

PubMed2025

OctopuSV and TentacleSV: a one-stop toolkit for multi-sample, cross-platform structural variant comparison and analysis.

MOTIVATION: Structural variants (SVs) influence gene regulation, disease progression, and diagnostics, yet integrating SV calls across platforms remains difficult due to inconsistent annotations, limited merging flexibility, and fragmented workflows. Ambiguous breakend (BND) annotations, which comprise many variant calls, are often discarded or misclassified, hindering variant characterization. Existing tools lack advanced merging operations essential for precise identification of disease-specific or somatic variants across samples or patient groups. Additionally, current SV analysis pipelines require extensive manual intervention and complex parameter tuning, compromising reproducibility and scalability. Addressing these gaps is crucial for improving the accuracy, interpretability, and clinical utility of SV analyses. RESULTS: We developed OctopuSV and TentacleSV to address these long-standing challenges in SV analysis. OctopuSV features a specialized BND correction module that converts ambiguous BND annotations into canonical SV types, recovering important variants that are often overlooked by existing tools. Additionally, it provides advanced set operations (difference, complement, custom-defined) that enable sophisticated variant filtering without programming expertise, critical for identifying tumor-specific SVs or variants unique to specific sample groups. TentacleSV completes our solution by automating the entire SV analysis process from raw sequencing data to high-confidence callsets, ensuring consistency and reproducibility across projects. Benchmarking across short-read and long-read platforms showed superior F1 score, complete SV type consistency compared to existing tools. Our framework enables experimental biologists and clinical researchers to perform sophisticated analyses ranging from cancer subtype-specific SV identification to multi-sample comparative studies without requiring specialized programming skills. AVAILABILITY AND IMPLEMENTATION: All codes are available at https://github.com/ylab-hi/OctopuSV; https://github.com/ylab-hi/TentacleSV.

Software↗

PubMed2026

Cost-Effectiveness of Electronic Patient-Reported Outcome Measure Interventions in Cancer: Systematic Review and Parameter Extraction for Economic Modeling.

BACKGROUND: Complex digital interventions that integrate electronic patient-reported outcome measures (ePROM) into clinical practice in cancer have the potential to improve quality of life, increase survival, and reduce health resource use and costs. Such systems can help patients with cancer self-manage chemotherapy symptoms, reduce clinicians' workloads through automated decision support, and resolve problems earlier. However, more research on the cost-effectiveness of ePROM monitoring is needed. OBJECTIVE: This paper comprises two complementary components: (1) a systematic literature review summarizing and evaluating the quantitative and qualitative evidence related to the cost-effectiveness of ePROM monitoring and (2) a health economic model parameter extraction. We also conducted supplementary targeted searches and scoping to provide context to our findings. METHODS: We searched Ovid (including MEDLINE and Embase), Scopus, and the International Health Technology Assessment Database for original English-language papers published on or before March 2025 using search strings that combined terms related to ePROMs, health economics, and cancer/oncology. We included papers reporting health economic-related outcomes for ePROM interventions designed for adult cancer populations and excluded screening tools and conference abstracts. RESULTS: We included 34 publications from 27 unique studies and identified and analyzed 26 ePROM-integrated interventions within these. Most (23/26) of the included interventions explicitly described some form of alert handling and automated decision support based on remote ePROM monitoring. Of the 34 publications, 5 presented full cost-effectiveness analysis results, of which 3 were highly uncertain and lacked clear differences in costs and health outcomes between ePROMs and standard care; conversely, 2 presented strong evidence of cost-effectiveness due to quality-of-life improvements, reduced hospitalizations, and potentially more autonomy in health-related travel (eg, ePROM-monitored patients can drive or walk to the hospital instead of using taxis or ambulances). A further 5 publications reported partial health economic results (eg, cost-consequence and budget impact), of which 1 detected no difference in strategies; in contrast, 4 reported lower health resource use and costs of ePROMs, mainly due to hospitalization reductions. Overall, 12 of the 27 studies included a qualitative component but mostly focused on user experience and design-related themes; only 2 of these addressed economic-specific themes (eg, changes in workflow and resource use due to ePROM implementation and integration), indicating some potential for time saving due to ePROM monitoring. CONCLUSIONS: Some ePROM-integrated interventions demonstrated cost-effectiveness in cancer care, but the evidence base remains limited. Where evidence does exist, cost-effectiveness appears driven by reduced hospitalization and improved quality of life. Qualitative research within the included studies rarely addressed economic questions. We provide a detailed parameter extraction for use in future economic modeling and recommend research priorities, including quantitative mapping of ePROM symptom data onto health resource use patterns, and qualitative work exploring how ePROM implementation affects clinical workloads and patient-perspective costs.

Humans↗

PubMed2000

Picture archiving and communication systems.

Picture archiving and communication systems (PACS) are extremely versatile systems that facilitate the transfer of digital images and patient data throughout a healthcare enterprise. PACS are most commonly used in radiology departments to process images from diagnostic imaging modalities, such as digital radiography devices and magnetic resonance imaging (MRI) systems. In this article, we evaluate three such systems from three of the largest PACS suppliers--the Agfa IMPAX, GE PathSpeed, and Siemens Sienet. The ultimate goal of any PACS is to improve workflow within the enterprise. Our testing focused on whether, and to what degree, the systems could meet this goal. We found that all three PACS are fairly complete and able to support image distribution and improve workflow compared with film-based processes. However, we did identify significant differences--along with some noteworthy limitations--with respect to system design and operation, external integration capabilities, and supplier support and professional services. We have ranked the systems for each of these factors to help healthcare facilities identify the system that will best meet their needs. Also in this Evaluation, we present an overview of the technology, a Glossary of PACS terminology (see page 400), selection guidance, and discussions of PACS-related topics, including data compression options, the role of the DICOM standard, and concerns about data security in many of today's systems.

Automation↗

PubMedDate not supplied

SynCar: an approach to automated synthesis.

The automation of all aspects of manual solution-phase synthesis into one integrated, efficient, and reliable system could be regarded as something of an unmet challenge in organic chemistry. The requirements for modern solution-phase libraries in mainstream drug discovery is typically 50-250 high-purity compounds on a 10-100-mg scale, whether for target class libraries or lead optimization, and short cycle time in combination with high capacity is critical. To achieve these goals, in a codevelopment between Aventis and Accelab GmbH, Kusterdingen, Germany, we designed a completely novel system of independent workstations connected by a shuttle transfer system produced by Montech, Derendingen, Switzerland. Seven modular workstations process four reactions on each shuttle in parallel, with the ability to perform synthesis (temperature control and liquid reagent handling), filtration, liquid-liquid extraction, evaporation, weighing, solid-phase extraction, and HPLC/MS analysis. The modular design enables the continuous loading of shuttles at any time, and each shuttle can have its own workflow. The design also allows easy expansion for future needs. The result is a combination of high flexibility and high throughput.

Chemistry, Organic↗