Knowledge Tools

Increasing role in drug development

Alan S Louie

Alan S Louie

Research Director Health Industry Insights An IDC Company USA .

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By using knowledge tools to better understand a potential new drug's effectiveness and safety early in the development process, it becomes possible to terminate potentially problematic drugs earlier, saving both time and money.

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Both academia and the health industry generate immense amounts of scientific data during their research efforts. As a result, the academic and industry researchers often find themselves in a position of having more data than they can effectively manage and interpret. In addition to data generated directly by researchers, huge amount of data is accessible from public or collaborative resources, generated by research programmes including the human genome and HapMap projects. A key factor in successful research programmes is the ability to effectively transform data into knowledge. In research, it is not uncommon to have gigabytes and even terabytes of data that require effective analysis. Managing these large amounts of data can tax even the experienced researchers.

The development of new drugs is a knowledge intensive process that requires a wide variety of information at each stage of development. With the increasing shift towards translational medicine and the use of biomarkers in drug development, knowledge is now playing an increasingly important role in efficiently advancing new drugs through the development process. This shift has been further supported by efforts to increase the historically low efficiency of drug development. By using knowledge tools to better understand a potential new drug’s effectiveness and safety, early in the development process, it becomes possible to terminate potentially problematic drugs earlier, saving both time and money. In addition, by leveraging emerging knowledge in genetics and genomics, it is becoming increasingly possible to identify likely responders and non-responders to drugs, enabling the basic promise of personalised medicine.

Concurrent with the growth in research data has been the growth in the development and commercialisation of software tools to simplify data collection, access and analysis. With global bioinformatics software sales at US$ 1.3 billion and growing at an annual rate of 7%, this market continues to be attractive for future investment. Including such areas as complex analytics, systems biology solutions, Laboratory Information Management System (LIMS), and workflow management, these bioinformatics software tools are enabling researchers to significantly improve productivity and streamline research. More detailed discussion of these knowledge based software/bioinformatics solutions is provided below.

Complex analytics

Software providing data analysis and visualisation are among the most mature of bioinformatics programmes. Typically built around well characterised and generally accepted analytical processes, including statistics, chemical modelling, and physical properties, these programmes embed formulas into easy-to-use packaged solutions that enable scientists and other researchers to rapidly transform data into knowledge. Key innovations in drug development analytics software include codification of increasingly complex biological knowledge into software applications, application of analytic solutions into the highly regulated clinical development environment, and improved connectivity between analytics applications and other drug development software (more details to follow below). Specific vendors contributing analytics software to the drug development industry include Accelrys, Insightful, IntelliChem, MDL, Mathworks, SAS, Spotfire and Symyx.

Systems biology solutions

Bioinformatics software tools have also increased in complexity to directly incorporate significant chemical and biological knowledge into programmes. These knowledge-based tools enable more complex analyses that can be used to create higher level interpretations. Systems biology software tools are representative of this type of drug development software tool. Commercial software solutions include pathway analysis software products by Ingenuity Systems, GeneGo and Ariadne Genomics; high throughput data driven systems biology model companies, including BG Medicine, BioSeek and the Icoria division of Clinical Data; and high level systems biology modeling solutions, including solutions from Entelos, Gene Network Sciences, Genomatica, Genstruct and Optimata.

LIMS

LIMS software enables direct connectivity between data generated by laboratory instruments and research databases. Early expansion of LIMS applications included streamlined automation of sample management and targeted applications focussing on use of LIMS in the QA/QC and manufacturing environments. Key recent innovations include linkage of experimental design automation to automated experimentation and expansion of LIMS applications to improve ease-of-use, including transformation of LIMS into a web-based solution and incorporation of specialised product templates into manufacturing LIMS to improve new product LIMS implementations. Specific LIMS vendors include ABI, Lab Vantage, LabWare, StarLIMS, Teranode and Thermo Fisher Scientific.

Workflow management

Workflow oriented bioinformatics programmes that automatically bring data together are important efficiency tools that free up researchers to concentrate on data analysis and interpretation instead of wasting time on repetitive time consuming supporting efforts.

Workflow software extends to enable connectivity with LIMS, data visualisation, and analytics software as well as a variety of data resources. Key innovations in workflow software include improvements in researcher interfaces to enhance ease-of-use and continuing efforts to expand connectivity between software applications. Key vendors in workflow management include IBM, InforSense and Scitegic.

Current knowledge tool software development

Knowledge-based bioinformatics software tools are increasingly addressing this need through improvements in the user interface, expansion of applications to incorporate new available research knowledge, and expansion of product offerings to incorporate application areas peripheral to the primary target market.

Current trends in knowledge tool software development include:

Connectivity, interoperability and improved access: With improved efficiency continuing to be a major driver for pharmaceutical R&D, streamlining of data processes is becoming increasingly important. Current efforts to improve connectivity and interoperability are enabling software applications to pass data back and forth seamlessly, enabling improved productivity.

Enabling semantics: While clearly an emerging informatics approach, the use of the semantic web in drug development is increasing. IT web enables incorporation of semantic relationships into data analyses and has the capability to enhance connectivity between disparate data resources.

Increasing complexity of systems biology product solutions: In drug development applications, systems biology promises to provide holistic understanding of disease, normal and diseased organ systems, and the human body as a collective system. Pathway analysis and high-throughput data driven systems biology approaches are beginning to incorporate the features of high level physiological models.

Increased bioinformatics applications for the clinical development environment: Consistent with other clinical development requirements, software must be fully validated to be used in the clinical development environment. This added level of stringency has limited the use of emerging bioinformatics software applications in clinical development. Leading knowledge based software applications in the clinical development space initially focus on adaptive clinical trials, but broader applications, including clinical data mining and genetic profiling are expected as personalised health efforts advance.

The future of knowledge tools

Over the long term, knowledge-based software tools are expected to become a core element of the drug development process. With the idealised goal of development of a comprehensive in silico human model that accurately reflects the potential impacts of a new drug (both positive and negative) on the human body. In the interim, there are a number of application and process innovations that will transform knowledge-based software tools in the future.

Application innovations

Key innovations that offer the potential to transform drug development include:

  • Development of in silico biology simulations, beginning with cell, organ and disease modelling supported by empirical experimental data and eventually developing into complex mechanistic models that reflect experimental biology
  • Increased complexity of visualisation tools, including complex 3-D applications and visualisation of high resolution molecular imaging data, all in near realtime
  • Full interoperability of software applications, including transparent data sharing between silos and across the web
  • Cradle-to-grave workflow connectivity, beginning with e-notebooks and enabling data connectivity through product commercialisation

Business process innovations

As global markets become increasingly skilled and accessible, commercial life science software development companies (primarily US companies) are increasingly able to leverage low cost development resources to competitive advantage. In addition to direct cost savings, it becomes possible to apply increased development resources to accelerate software development. Looking forward, key considerations from the business perspective include:

Drug development software tools will play a growing role in biotechnology and pharmaceutical R&D

  • Contributions from software tools are increasingly becoming important key components of regulatory submissions, resulting in more robust applications with higher potential for approval and potentially shorter review times
  • New software applications will continue to originate from leading academic life science research with increasingly rapid productisation

Conclusions

Technology innovation, practical business drivers, and the shift towards more mechanism- of-action based drug development ensure that knowledge-based software tools will play an increasingly important role in drug development for the foreseeable future. Future growth is assured and will occur through systematic expansion of product offerings towards more comprehensive solutions with global influences potentially changing the process of software development.

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