Image courtesy of IDBS
The pandemic exposed the innovation divide between the digitally transformed and those that lagged. Strict regulation made life sciences and bio/pharma organizations hesitant to modernize too quickly away from proven legacy methods and technologies, resulting in varying levels of digital transformation. But since the pandemic, organizations now recognize the necessity of digitalization and smart automation to accelerate research and drug development.
Freeing up and transforming researchers’ time
Companies that haven’t embraced the digital revolution, including digital (process) automation, tend to burden scientists with repetitive, manual work — like pipetting and recordkeeping. Digitally automated systems perform such tasks in more digitalized companies. Companies embracing future-facing technologies are investing in automation and digitalization, allowing for virtual remote access to dry and wet labs, which enable a scientist to define the parameters of their experiments and program the instruments needed to execute them and record the results automatically. The researcher can then analyze the data in real-time and decide whether to continue the experiment. Automation frees up scientists’ time so that they can focus on cerebral activities: thinking and making informed, strategic decisions.
Physical automation is just one-half of the equation. Software — how you store, manage, and apply your data — is the other critical piece. Humans can process only so much data, whereas sophisticated software incorporating artificial intelligence (AI) and machine learning (ML) can detect patterns and reveal insights from data with high throughput and accuracy that exceeds human capacity. But AI and ML need to be trained using huge amounts of specific, aggregated data, requiring robust data foundations, efficient processes and expert knowledge. These requirements also apply to the data that the AI/ML will be used on.
To this end, companies are embracing the software as a service (SaaS) model, leveraging dedicated expertise and experience from vendor partners. For instance, dedicated digital software solutions such as biopharmaceutical lifecycle management (BPLM) offer gains in overall efficiency and cost-effectiveness through smart automation of repetitive, error-prone manual tasks and the curation of data needed to tackle the biggest challenges in process design, optimization, scale-up and technology transfer. Over time, data-driven insights can also lead to better study design, using tools such as in silico modeling and digital twins, which increases right-first-time development efforts and reduces the number of experiments needed. Moreover, BPLM platforms and their vendors can support companies in overcoming the hidden challenges in biopharma development. Improvements delivered across the biopharma lifecycle by BPLM can accelerate a drug’s time-to-market by three years [1,2].
Companies that don’t adopt SaaS will fall behind, not only from the overhead of maintaining and upgrading software in-house but because they will be unable to accelerate the drug development process. In other words, organizations that don’t shift the burden to the vendor are slowing down innovation and drug development itself.
Building in compliance
Another benefit of digital transformation by a BPLM offering is that regulatory compliance is built into the process. Compiling data for regulatory submissions is a major bottleneck in the biopharma lifecycle. Automated instruments and smart software like BPLM can manage the flow of data and ensure its integrity, logging potential compliance violations as data are captured and aggregated in real-time, thus allowing scientists to investigate, resolve and report them promptly. This means, in essence, that compliance is also monitored in real-time, significantly reducing the quality burden typically faced by life sciences and biopharma organizations. In addition, the application of robust data foundations and data capture and recording in integrated data formats maximizes the utility of the data. For example, it makes it easier to compile data from multiple experiments and sites for regulatory submissions. Furthermore, BPLM platforms and the best practice workflows that they offer can also build in their compliance, method validation and their, consistency, quality, and reproducibility for pharma applications [3].
Working from home is here to stay
Before the pandemic, the idea that scientists could work remotely, just as many other workers can, was largely “unproven” and thus very rare. But the pandemic proved that organizations that were further on the digital transformation path could indeed support hybrid and remote working conditions for scientists. These companies continued innovating through new scientific methods, technologies, and instruments. It turns out that innovation isn’t stifled by remote working as once thought, even for scientists. In a post-pandemic world, employees are demanding more flexibility, researchers included. And the digital transformation has enabled this: smart automation makes it possible for scientists to perform better and “lights out” science, while sophisticated data management allows for more streamlined, insightful analytics that can be accessed from anywhere in the world. Digital transformation, including smart automation, should facilitate faster drug discovery and commercial development, with “fail faster” detection systems and accelerated time-to-clinic for millions of patients worldwide.
Rehan Malik
Rehan Malik is the Global Head of Solutions Consultancy at IDBS. His long-standing career spans over 20 years in biopharma and the healthcare industry, where he consulted organizations on implementing enterprise software solutions fueling their digital transformations. He holds a B.S in Computer Science from Seton Hall University, New Jersey.
References
- IDBS Polar. Hidden Challenges in Biopharma Development. Infosheet: BPLM. https://www.idbs.com/2021/09/infosheet-why-bplm/ (accessed Jan 2022).
- Wouters OJ, McKee M, Luyten J. Estimated Research and Development Investment Needed to Bring a New Medicine to Market, 2009–2018. JAMA. 2020; 323(9): 844–853.
- IDBS Polar – Bioanalysis. IDBS Polar BioAnalysis enables CROs and biopharma to improve quality and reproducibility, reduce study cycle times and accelerate time-to-market. https://www.idbs.com/polar/bioanalysis/ (accessed Jan 2022).