Joe DiMartino (Luminata Solution Manager, ACD/Labs)
At a time when the world is watching, and expecting lightning fast results from drug development for SARS-COV-2 therapies, I am reflecting on the impediments drug development teams face in releasing an antiviral drug quickly while keeping safety and quality at the forefront.
Based on past trends, only 5 out of 5000 investigational new drugs make it to clinical trials, and subsequently it takes 10 to 15 years to release a drug to market.1 There are exceptional drugs that have been fast tracked, such as Imatinib, which took three years from Phase I to NDA submission,2 but this an exception rather than the rule. For the majority, the question remains: How can we accelerate more cases for new drugs? Here I discuss specific bottlenecks within the drug development process and how to reduce the time of various drug assessments.
Re-engineering the Quality of Management Tools
In order to deliver a drug product to clinicians, pharmaceutical development organizations employ a variety of quality assurance practices, culminating in a “review and release” step. The underlying quality management systems which support adherence and compliance with established QA practices3 require extensive document preparation efforts by the development project team stakeholders.
This project timeline “Critical Path Activity” can be a significant risk factor for completing pivotal clinical trials, and ultimately bringing safe and effective medicines to patients in need. This is especially pertinent today, when considering the aspirational goal of reducing the clinical trial and regulatory submission/approval timelines to support especially urgent patient needs (like COVID-19 infected patients).
We believe that Luminata® allows for more efficient data-driven decision support and health authority filing compilation efforts than current workflows. Relevant Luminata capabilities include:
- Improved data accessibility via automated integration, where the Chromatography Data System (CDS) is directly linked to our software for reporting or further analysis of the processed data
- Dynamic visualization of assembled and compositional summaries for:
- Risk assessment conclusions pertaining to impurity onset, and fate and purge
- Comparative assessments of different purification methods
- Comparative assessments of different control strategies
- Facile incorporation into Microsoft Office Documents
- Automatically generated forced degradation maps and trend plots, where the software takes a theoretical degradant targeted list, and automatically performs batchwise processing of mass spectrometry data. Once completed, a data-directed degradant map is generated; whereby, all related mass spectra are associated to each observed degradant. Moreover, the observed degradation components are grouped by exposure conditions, allowing for decision makers to rapidly assess any risks, and ultimately guide downstream stability tests, handling/storage requirements, and expiry period determinations.
Human Operations to Support the Supply of Materials
How can we improve efficiency in the supply chain? Currently, supply chain systems focus on production planning, demand planning, and location of material batches. Innovative stakeholders must not just look at legacy technologies but venture out towards technologies that can reduce their current time and task constraints.
To get a better understanding and value of all batches, specifically clinical trial batches, stakeholders should take into account both contract manufacturers’ batches as well as batches created in-house. An improvement in efficiency can be as simple as allowing a chemist to view a comprehensive batch genealogy of the drug product across the entire supply chain. The image below shows one example of connecting the batch family tree of a project with all batches of external manufacturing. Additionally, the analytical data is not typically stored within the batch. This information is traditionally managed in static Microsoft Excel worksheets, with all supporting batch analytical data locked into CDSs and/or ELNs. Luminata stores the analytical data within the batch and provides dynamic visualization of the process scheme and batch genealogy family tree. This allows for real time, data-driven batch-to-batch assessments, that can easily be used to complete investigations of alternative catalysts and reagents for the same API endpoint.
The batch genealogy tree (top) helps those involved in clinical batch manufacturing to view how each drug substance/drug product batch is used in subsequent synthetic stages. Related analytical data (bottom) can be viewed by clicking on each batch (indicated by the yellow coloring of Batch 305).
For clinical batches, data access in one place is a key requirement for scientists and an important capability afforded by Luminata. That is, storing all reaction information within a batch alongside analytical data. This can greatly help drug supply manufacturing and help prevent possible recalls of commercial batches to the general public.4 Luminata offers a “manufacturing process-to-data” association that may indeed help Drug Development stakeholders facilitate an overall acceleration of various Critical Path Activities.
If you’re interested in learning more, please visit our website, or contact us to discuss how we can help you digitalize your drug development workflows.