We help companies embrace the digital thread and leverage technologies to fundamentally improve the way they discover, create, make and sell therapies.
The pace of change in the pharma and biotech industry driven by the COVID-19 pandemic is both exciting and extraordinary.
In a traditionally conservative and highly regulated industry with famously long product development cycles, the pandemic has shown the need for dramatically increasing operational flexibility and reducing time to market.
Companies have been forced to reconsider their processes and broaden their digital capabilities in order to quickly pivot product portfolios, development, manufacturing and campaign fulfillment.
Establishing a digital thread across the drug product value chain creates opportunities for companies to accelerate product development timelines, reduce manufacturing latencies and improve integration with quality and regulatory information management systems. Ultimately, enabling innovators to accelerate speed to market for therapies that improve patient lives around the world.
Our Pharma & Biotech Clients
- Small molecule
- Cell and gene therapy
- Combination products
- In-vitro diagnostics
- Nutraceutical, vitamin and nutritional supplements
- Contract development and manufacturing organizations (CDMO)
We helped a leading pharma company speed time to trial and patent approval, scaling their portfolio by over 300% with greater compliance.
We drive results for pharma, biotech and CDMO companies across the value chain, from molecule discovery through pharmacovigilance.
Generate Topline Growth
By increasing innovation and accelerating time to market
Drive Bottom Line Improvements
By creating operational efficiencies
Reduce Invested Capital
By leveraging single-use technology
Power the Connected Enterprise
By converging information technology (IT), operational technology (OT), and data science
As strategic partners to some of the largest, most complex pharma, biotech and CDMO companies in the world, we are driven by the core goals of curing conditions, eradicating disease and improving patient lives.
Our work is hands-on, and our knowledge is based on our experience as industry practitioners. We are committed to helping you navigate the industry’s complex regulatory landscape and deliver high-quality medicines to maintain health and enhance patient outcomes.
Pharma, Biotech, CDMO Service Focus Areas
Technology Partners & Industry Associations
We helped a leading pharma company enhance technology transfer and reduce time to clinical trials by implementing digital knowledge management for small and large molecule substances, advanced biologics, targeted drug delivery devices and combination products.
Phase 1. Our client’s initial goal was to dramatically improve the lead optimization phase, by managing the knowledge during the synthesis of lead compounds, identifying new analogs with improved potency, and reducing off target activities. The client had divested their pilot plants and wanted to accelerate getting new compounds ready for clinical trials and commercialization by leveraging CDMOs to reduce time to market, cost, and improve speed to clinical trials.
The client was experiencing knowledge sharing, collaboration challenges and inefficiencies with the CDMOs due to difficulties in exchanging product requirements and critical formulation data, which was stored in paper-based documents and siloed, externally inaccessible systems. They realized that without the ability to easily and securely share relevant materials and process critical quality requirements for how the product should be produced, they would not be able to achieve the targeted time and cost benefits of partnering with CMDOs.
We worked with our client to implement a digital knowledge management solution, creating a unified repository for market requirements by region, materials, process models, recipes, site and market variations, with clear traceability to each market specific SKU.
Our work made it possible to capture and manage knowledge digitally, which improved collaboration internally and externally across the value chain, accelerating technology transfer, speed to trial and product approval.
Phase 2. Capitalizing on the momentum of digital knowledge management in small molecules, we then shifted focus to enabling the same capability for their drug delivery devices and combination products business units. Because the company was first and foremost a medicines company, established practices were rooted in drug development and we had to adapt and expand them to incorporate discrete device product development management. We worked with our client to implement a solution that manages requirements, product data, CAD data, and risk under change control.
We tailored our approach to encompass not only establishing the right systems, processes and data, but also education and shifting mindsets to align with leading practices in medical device development.
Phase 3. We are now working with our client to build additional digital mechanisms beyond digital knowledge management to further streamline technology transfer and scale up. A technology transfer hub is being established to map ISA 88/95 data structures across the extended network and also publish standardized recipe to internal and partner downstream systems such as ERP and MES. This improves change propagation across sites and systems enabling more efficient feedback, recipe data publication and collaboration.
Together, we enabled:
- Effective collaboration across an extended manufacturing network
- Streamlined sourcing
- Reduced manual effort and risk
- Accelerated speed to clinical trials
- Faster development in molecule pipeline
- Drug development quality management
- Data-driven decisions on network performance
We helped a leading biologics company reduce batch cycle times by 30%.
Our client sought to improve the quality and performance of their production processes with real-time analytics insights.
Existing manufacturing processes were based on manual sampling methods resulting in extended batch cycle times and sometimes leading to scrap.
We deployed predictive models that assess real-time independent variables (inlet air temperature, exhaust air temperature, and fan speed) and dependent variables (ideal moisture level) with a machine learning model trained with historical performance data to continuously predict ideal moisture targets with 95% confidence.
Operators now have real time decision support model-based predictions for more reliable outcomes with improved batch cycle times.
Our work improved process control, production rate and product quality, while reducing batch cycle times by 30% and scrap or waste by 5%.