LIMS may help with obstacles related to medium- and high-throughput ADME screening.
With the advent of parallel synthesis and advances in bioanalysis, a wide variety of in vitro Absorption, Distribution, Metabolism and Excretion (ADME) profiling screening assays and methods are being employed at an increasingly faster pace. This facilitates efficient drug candidate selection and optimization of a compound’s ADME and physicochemical properties.
Within Pfizer’s Pharmacokinetics, Dynamics and Metabolism department (Pfizer PDM), data was being used to contribute to the knowledge of ADME properties, but researchers were left with a dilemma: how to improve the throughput cycle from experiments through data, into knowledge.
The wealth of data generated by ADME studies challenges the information handling and processing capabilities of most pharmaceutical and research organizations.
Since knowledge is the greatest asset of any drug metabolism department, rapid and broad dissemination of the ADME data to project teams is critical. Access to such data maximizes the impact and understanding of ADME sciences, while minimizing ADME attrition. These results can also serve as the foundation for building reliable in silico ADME models. Moreover, global data-sharing platforms are instrumental in increasing the impact of data beyond a particular project and leverage the global scale of large pharmaceutical companies. These demands, combined with the large numbers of compounds that need to be analyzed, have resulted in the need to streamline ADME profiling further.
Historically in Pfizer PDM, the lack of a unified approach for collecting and reporting in vitro ADME data resulted in the use of multiple, local, in-house developed systems to upload data into global data warehouses. These disparate solutions increased the potential risk of errors and led to inconsistencies in processing, interpreting, and handling of data. Furthermore, only end point data were uploaded and stored in a central, corporate repository; data behind the end points were scattered in disparate locations without traceability and no real-time value. Without a single, accessible database that incorporated all processed and raw data, Pfizer PDM was limited in their ability to share data.
In addition, point solutions, such as standalone spreadsheets and graphing tools, were not harmonized across the company and were inefficient and hard to maintain, resulting in inconsistent practices and results for in vitro ADME data processing and analysis. Data was not easily integrated with other technologies and systems employed in the drug discovery arena.
Setting LIMS specs
An enterprise-wide laboratory information management system (LIMS) was needed to reduce effort and manpower, while increasing throughput by automating complex experiment templating and instrument interfacing as well as data analysis, reviewing, and acceptance tasks.
Pfizer PDM established certain criteria for its LIMS for ADME analysis (see sidebar) including rapid importing of large amounts of HTS data; graphical and tabular views of data for quick review; and automated processing of results that flags suspect data. Pfizer PDM selected Thermo Scientific’s Galileo LIMS and influenced the development of Galileo as features were designed to accommodate Pfizer PDM’s best business practices.
A Rapid Study Entry (RSE) workflow process transforms measured analytical data from LC/MS instruments, into in vitro ADME calculations and graphics for high-throughput screening. Data are acquired and converted into a Microsoft Excel spreadsheet-formatted file for integration into established assay systems. While importing data, users can create new studies and projects and populate the template’s design with actual study data.
Study design templates allow specification of key experimental variables that are typically held constant such as species, biomatrices, tissue, analytical time points, CYP substrates, and assay methods. The RSE file format brings in the variable experimental details, such as drug ID and concentration, analytical measurements, CYP metabolites and other experimental information. With RSE, insertion of new compounds into the database and creation of analytical runs are also enabled. These compound numbers and batch lots can be then easily selected for future non-RSE experiments).
Calibration curves may be incorporated using various regression algorithms to convert peak areas into concentration values. This step is not employed if LC/MS peak area data are used directly for in vitro ADME calculations such as metabolic stability or permeability.
Visual data can be reviewed in tabular, thumbnail, or detailed formats. In the thumbnail view, users can scan across many datasets; in the details view, they can drill-down. Results are automatically calculated when the charting galleries are opened and re-calculated whenever outlier points are deactivated. ADME parameter results can be flagged automatically in the details view based on pre-defined criteria to reduce user intervention. These criteria are applied by an automated results-flagging feature that allows the creation of customizable template-specific scripts that can suggest interpretations of the data. These can be overruled on a compound-by-compound basis. The automated-results-flagging feature applies green, yellow, or red flags to indicate the status of results as acceptable, questionable, or rejected, respectively, for each separate drug candidate. Users can navigate to specific data sets that need manual intervention.
Since only questionable technical data needs detailed review (typically 10% to 50%), less time is spent reviewing data, and more time is available for interpreting results and creating knowledge.
LIMS for ADME screening
Pfizer PDM uses LIMS to conduct a wide range of in vitro high-throughput ADME assays, including metabolic stability, enzyme inhibition (cocktail, single probes), permeability (Caco-2 cells, MDCK/MDR1 cells, PAMPA, cornea), and protein-building (equilibrium dialysis and ultrafiltration).
Tier-1 high-throughput ADME screening is centralized globally within the company. Thousands of compounds are screened each week for several assays such as PAMPA, metabolic stability, and single concentration CYP inhibition. Customized legacy processes are used for sequence generation for the rapid study entry process enabling high-throughput LC/MS analysis and data review. The output is custom-reformatted for rapid study entry, which is suited for high-throughput ADME screening since it leverages existing high-throughput customized processes.
ADME Tier-2 screening is site-distributed within the organization. This secondary examination of ADME properties is performed for dozens to hundreds of compounds per week. Custom workflow management tools are used. LIMS is used for sequence generation, for data processing by analysts, and for traditional data review. In addition, it is used in the drug development group for definitive ADME calculations.
Pfizer PDM uses the automated results-flagging capability process permeability assay data. Scientists examine the leakage marker, first determining whether the Papp is too high. The Papp ranges are then checked for control compounds and flagging markers that are out of tolerance. Checks for test compounds are then carried out. Once limits have been set on the selected group of test compounds, the coefficient of variation is checked for replicates, and disparate data points are identified and rejected.
Pfizer PDM also carries out checks for metabolic stability. First, any interference from the matrix is identified by performing a no drug control. Next, scientists check the chemical stability of the compound and the degree to which the compound is metabolized by non-P450 enzymes. The compound is also checked for the R-squared value for fit and the slope value is identified. A positive slope can indicate time-dependent solubility. Different R-squared limits can be applied depending on the slope allowing greater faith in the suggested interpretation. Finally, a check for stable compounds is carried out, identifying compounds where the half-life is greater than the limit.
A purpose-built LIMS can facilitate high-throughput ADME profiling and screening by providing an integrated system that allows rapid data review and acceptance for large numbers of compounds. Pfizer PDM has been able to manage data more efficiently and rapidly execute key decisions on drug candidate selection by getting the data to the projects as quickly as possible.
About the Authors
Lead authors Jenkins, Samoil, and Usansky were supported by Marcel Hop, Tim Letby, Todd Archer, and Kevin Whalen of Pfizer Global Research & Development, La Jolla, CA.
This article was published in Drug Discovery & Development magazine: Vol. 10, No. 11, November, 2007, pp. 36-40.
Filed Under: Drug Discovery