BU-NOT-SET

The promise of pharmaceutical sciences is to accelerate the development and introduction of life-changing treatments for previously untreated diseases, to improve therapies with better drug products, and to ensure they are affordable and accessible. Nevertheless, the reality of pharmaceutical development frequently does not match this potential. Scientific complexity, the possibility of clinical trial failure, as well as challenges achieving regulatory approval, all result in lengthy timelines for introducing a new medication to the market, with implications for cost and risk. 

How can these threats be addressed to streamline and accelerate drug development, and increase the chances of commercialisation success? The answer lies in harnessing data effectively. Innovations in digital connectivity, modelling and predictive analytics could all hold the key to tackling drug development challenges, enabling your project to overcome the innovation bottleneck.

Understanding the obstacles

For any pharmaceutical company - regardless of its size or internal capacity - introducing a new medication to the market is a significant undertaking with many obstacles and setbacks. 
Some of the greatest challenges facing pharmaceutical companies in getting their innovation from discovery to delivery to patient include:

  • Extended timelines: It takes between 10 and 15 years1 and more than $1 billion to bring a new drug to market. 
  • Scientific complexity: From identifying the right biological targets to determining the most effective drug delivery approach, challenges in scientific and technological capability hinder drug discovery and lead to costly failures.
  • Pharmaceutical development: The selection of the formula, manufacturing process and dose form often doesn’t reflect all properties of the active pharmaceutical ingredient (API), or is not done with the context of biopharmaceutical behaviour taken effectively into account. 
  • Clinical trial risks and complications: Recruiting patients, designing robust trials, setting the right clinical endpoints and interpreting complex data are just some of the obstacles faced in clinical research, increasing the risk of failure in trials. 
  • Challenges in achieving regulatory approval: Navigating the stringent requirements and ever-evolving landscape of regulatory approval can be a daunting task – if the paperwork and data aren’t considered from the beginning, there are potential implications for timelines. 
  • Commercialisation hazards: Ensuring that the manufacturing process for the new drug is scalable to commercial volumes can be a challenging prospect.
     

The value of data in overcoming development barriers

Management of data plays a crucial role in overcoming the challenges inherent in drug development - provided companies can access and utilise it effectively at the right points on their innovation journey for it to have an impact. 

Access and connection of a wider range of rich data from different functional areas can support drug development with more accurate and informative predictive modelling. This entails using computational and mathematical tools to forecast the properties and behaviour of drug candidates. This can support the identification of promising biological targets more efficiently, reducing the time and cost spent working on non-viable candidates. 

Data and predictive modelling can also have positive effects in the realm of development design, supporting the implementation of quality-by-design (QbD). It can enable researchers to optimise the designs of their clinical trials, improve patient recruitment and endpoint selection, mitigating the risks and complications that delay trial completion or lead to trial failures. Furthermore, data can facilitate regulatory approval by streamlining documentation and ensuring compliance with evolving requirements. 

Digital connectivity supports the collation of data for richer insights

Traditional data management approaches, with their data fragmentation, manual data collection, errors and delays, are inadequate for modern pharmaceutical drug development and manufacturing, hindering access to information to support analysis and modelling.  

Thanks to innovations in data infrastructure, supported by new digital connectivity tools to integrate disparate systems from the lab, to the clinic, to manufacturing lines, it is possible to overcome these issues. Data can be effectively harnessed from across the drug development journey to be fed into advanced modelling and simulation technologies, making such models even more accurate and precise. 

By leveraging advanced analytics of this rich data and visualisation tools, companies can identify trends, patterns and correlations that would be impossible to detect with siloed data. This allows companies to truly understand their project in a way that empowers them to make decisions that help optimise finished product performance and streamline timelines. It further allows them to understand the impact of variabilities in materials and processes on the quality attributes of a drug product.

ReciPredict from Recipharm is one example of an advanced analytical solution that can deliver these benefits to drug development projects. 

The value of ReciPredict

ReciPredict is a platform that harnesses data science and digital technology to drive data-driven decisions in the development and manufacture of drug products. It combines rich data with statistical modelling and simulation to enable QbD at all stages of drug development. This includes initial formulation development, tech transfer, commercial manufacturing and product development.

By linking critical process parameters and material attributes with drug product quality attributes, ReciPredict creates statistical models that provide a comprehensive understanding of the product and accurately predict optimal quality outcomes.

The benefits of ReciPredict include:

  • Accelerated drug development timelines by reducing the number of test cycles
  • Cost savings by reducing API consumption
  • De-risked technology transfer by identifying ideal parameters for optimal process robustness
  • High-quality results achieved consistently.


Working with experts to harness the value of data

By harnessing the power of data and digital connectivity, companies can unlock unprecedented efficiencies and accelerate the development of life-saving treatments. Working with experts like Recipharm that can provide ready-made predictive modelling platforms, such as ReciPredict, companies and their drug development projects can benefit straight away. 

To find out more about ReciPredict and how it can benefit your project, read the fact sheet now, or contact our experts

 

Reference:

  1. https://www.ncbi.nlm.nih.gov/books/NBK22930/#:~:text=Several%20years%E2….