The future of drug development: Integrating open source and commercial software

Clinical trials for drug development are becoming increasingly complex, with numerous variables that must be identified and defined from the start. This complexity arises from rigorous study design, evolving regulatory requirements, diverse patient populations, and the significant time to conduct trials.

To address these growing challenges, biotech and pharma companies often choose between commercial simulation tools and open-source software. However, an alternate approach leveraging both commercial and open-source software can mitigate risk, transform drug development, and accelerate time to market.

Open source in pharma

To fully understand the potential of combining commercial and open-source tools, we must first explore the growing role that open source is playing in the pharmaceutical industry.

Since the introduction of the Open Source Initiative (OSI) in the late 1990s, statisticians have increasingly embraced the opportunity to contribute to and utilise open-source software. This is evident from the gradual shift from SAS software to the open-source R programming language.

The collaborative spirit of the open source movement, first established in the tech world, is now increasingly influencing the biotech and pharmaceutical industries. There has already been a notable shift towards open source in drug discovery and development, including initiatives like the Open Source Pharma Foundation, which advocates for open source approaches to pharmaceutical research and development.

A key advantage of open source is its potential to facilitate rapid innovation. Developers and researchers from diverse backgrounds can quickly evaluate and validate new algorithms. Additionally, by democratising access to advanced tools, open-source software enables smaller biotech companies and academic researchers to participate in novel drug development and contribute to an innovative ecosystem.

However, adopting open-source tools in drug development has brought several challenges. These include:

1. Lack of industry support and documentation – Open-source tools often lack the extensive support and comprehensive documentation that commercial tools offer. This often leads to a steep learning curve and a higher barrier to entry.

2. Concerns about reliability and validity – Without the rigorous testing and validation that commercial tools undergo, reliability can become a concern. Though many open- source software packages include extensive testing, there is no guarantee that this will persist over time. The challenge is exacerbated by the need to meet regulatory standards, as well as the long timelines that are inherent in drug development.

3. Unclear financial incentives – The lack of clear commercial incentives can limit the ongoing development and support of open-source tools. This can also create conflicting incentives for developers and pharmaceutical companies, sometimes discouraging commercial software development due to the availability of limited open-source alternatives.

A hybrid approach to drug discovery

Drug developers can overcome these challenges by adopting successful hybrid approaches taken from the tech industry.

In cloud computing and AI, combining open-source components with commercial solutions has proven highly effective. Companies like Google, Amazon, and IBM have seamlessly integrated third-party open-source tools such as Apache Kafka and PostgreSQL into their enterprise offerings. These frameworks bring innovative, community-driven development to commercial platforms that ensure enterprise-grade reliability, user support, and regulatory compliance.

Pharma is beginning to adopt similar strategies. A notable example is AlphaFold, the open-source protein structure prediction tool developed by DeepMind. While AlphaFold’s core model is freely available, companies like Schrödinger have enhanced its utility by integrating it into their commercial drug discovery platforms.

These platforms combine AlphaFold’s predictive power with proprietary algorithms for virtual screening, molecular dynamics, and lead optimisation, providing pharma companies with powerful, regulatory-compliant environments for drug discovery.

By blending the strengths of open source innovation with the reliability and scalability of commercial solutions, companies can streamline drug discovery and bring new therapies to market more quickly.

Clinical trial design

While there are currently few examples of clinical trial software utilising a hybrid approach, the potential is significant. Open source R packages like multiarm for adaptive trial design could be integrated into commercial platforms that offer user-friendly interfaces, visualisation tools, and regulatory compliance features.

Such integration could enhance simulation and optimisation by leveraging cloud computing, leading to faster response times and improved trial designs. By merging open source insights with commercial optimisation tools, pharma and biotech companies can accelerate the development of novel solutions for more efficient and effective drug development.

The future of drug development

As we look to the future, the most effective clinical trial designs will undoubtedly arise from a hybrid approach, integrating the strengths of both commercial platforms and open-source software.

However, the success of this approach will rely on strong collaboration between commercial entities and the open source community. By fostering partnerships and ensuring compatibility, both sectors can drive innovation and create more effective tools for drug development.

By leveraging open-source tools, hybrid approaches can also help reduce the overall cost of drug development, making it more affordable and potentially accelerating the time to market. This cost reduction could translate into greater accessibility to new therapies for patients worldwide.

Such an approach not only enhances efficiency and innovation, but also ensures that clinical trials are conducted with the highest standards of ethical and scientific rigour. As the pharma and biotech industries continue to explore and adopt hybrid models, we have the opportunity to significantly benefit patients and advance drug development.