Cyprotex’s Discovery Bus is an integrated IT solution for the drug discovery industry which automates decision making and information processing. It enables the steps carried out by a human expert to be modeled as a workflow and the decomposition of a more complex task into series of linked steps, each step of which can be implemented by specialist programs known as agents. Intelligent workflow techniques identify when an agent should be called and which tasks are appropriate for an agent to solve. A collection of specialist agents work together in a highly organized and efficient manner with other agents providing management and control.
The applications of the Discovery Bus are numerous and extend to a number of different industries. However some examples of the main applications which are relevant to the Drug Discovery industry include auto-QSAR and laboratory workflow processes which streamline the flow of compounds into assays and, subsequently, the capture and interpretation of instrument data.
Let us talk to you about how the Discovery Bus can help your business. We are keen to collaborate with external partners to trial and develop the Discovery Bus further.
The Discovery Bus is a novel and powerful software platform designed to enhance efficiency in Drug Discovery by automating human processes using intelligent workflow.
What is unique about the Discovery Bus?
The Discovery Bus can be applied to numerous different scenarios and industries to improve efficiency. Within the Pharmaceutical industry, a dramatic change is required to reduce the escalating cost associated with R&D. Two specific areas where the Discovery Bus can have significant impact include applications for in silico predictive modeling and laboratory workflow processes.
i. QSPR Modeling
In silico predictive technologies are growing in popularity as the awareness to reduce costs in drug discovery increases. This is typically performed by human experts making key decisions about which type of modeling techniques are employed, which molecular descriptors to use and which is the most appropriate set of descriptors to ensure an uncorrelated feature set is selected. Figure 2 illustrates the typical workflow for QSPR model building used.
QSPR model development can be subjective and labor intensive. Rework of the model is required every time new data is available and capacity is limited by human resource. The Discovery Bus is closely integrated with the generation of experimental data. It is programmed to search exhaustively for the optimal QSPR model and continuously update when new data becomes available. Although the Discovery Bus has a number of well known and reliable model building techniques in-built within the system, in-house favored techniques can be easily introduced.
The performance of the Discovery Bus has been evaluated using previously published datasets. The results shown in Figure 3 demonstrate that the automated QSPR modeling process implemented on the Discovery Bus can reproduce previously published QSPR models generated by human experts for two ADME properties, aqueous solubility and human serum albumin binding. This success occurs with full automation and no human decisions about model building techniques, descriptor selection or filtering methods thus reducing the prejudice and drastically increasing speed.
The Discovery Bus has machine learning capabilities which enable the most successful modeling techniques in previous assessments to have increased priority for future evaluations. It frees the time of the human expert to allow them to concentrate on the design and implementation of novel algorithms rather than the repetitive processing of information.
ii. Automation of Laboratory Workflow
Sophisticated instruments are now widely available for liquid handling and analysis which enable rapid screening and data generation for large numbers of compounds. As a consequence, the bottleneck has now shifted to the stage of data and information handling which traditionally is highly resource-intensive. Intelligent workflow plays a major role in meeting this challenge.
Expert knowledge is typically based on experience and often not shared within organizations. Building workflow plan specifications requires a compilation of knowledge with best practices implemented. Utilizing human resource is expensive, time consuming and subject to prejudice. Automating workflow processes addresses these issues by ensuring improved data consistency with rapid and reliable timelines and ultimately provides a cost effective solution. An example of a laboratory workflow is illustrated in figure 4. The Discovery Bus can capture and automate all aspects of this workflow.
About the team
Cyprotex’s team of software and systems engineers work closely with the Cyprotex scientific team. This has been a key component in enabling the current Cyprotex operation to deliver thousands of ADME screening results per month to our customers worldwide. The team was recruited from a number of software consultancies and ISPs and have a wealth of experience in software development and operation across a wide variety of industries.
The team at Cyprotex is led by Dr John Cartmell. Prior to joining Cyprotex, John has worked in a number of different roles. During his time in the theoretical computer science group at the University of Edinburgh, he published several papers on theoretical foundations including algebraic aspects of logic, algebraic descriptions of databases and of programming languages. For many years he specialized in development of computer aided software engineering (CASE) tools and meta-CASE tools based on an algebraically enriched database. The CASE technology was used on projects as diverse as the software and systems design phase of the European Fighter Aircraft and a year 2K systems rewrite conducted by an international bank. As chief designer John worked with and joint architected systems for numerous banks, insurance companies, wholesalers and other organizations. He has at one time worked as a consultant for the CCTA and he was also a member of a core design team for a UK-US collaborative effort to standardize environments for whole process support of software engineering projects. His latest project has been the development of the Cyprotex Discovery Bus and its further evolution into an integrated auto-QSAR system.
1 Cartmell J et al. (2005) J Comput Aided Mol Des 19; 821-33
2 Cartmell J et al. (2007) Curr Opin Drug Discov Devel 2007 10; 347-52