Artificial intelligence (AI) is driving a transformative wave in the pharmaceutical and biotechnology sectors. Its integration into drug discovery, diagnostics, and clinical trials is reshaping how we approach healthcare. Its ability to analyse vast datasets is enhancing disease understanding, boosting diagnostic precision, expediting drug discovery, and optimising clinical trials.

AI’s rapid growth highlights the need for robust intellectual property protection in these industries. Developing new drugs is a time-consuming and costly endeavour, with the majority of submissions failing FDA approval. Bringing a single drug to market costs around $2.3 billion.

How is Artificial Intelligence Impacting the Pharma Industry?

The practice of applying AI, particularly machine learning and neural networks, is reshaping the drug development industry. It significantly cuts costs and saves time. AI has the potential to be applied across various stages, such as predicting protein structures, streamlining compound screening, and uncovering patterns in high-throughput screening data. It can even generate new compounds tailored to specific targets.

AI’s role in drug discovery promises more effective therapies, with AI-identified compounds already undergoing clinical trials. As the pharmaceutical industry evolves, AI offers invaluable tools to expedite life-saving drug delivery.

AI in Drug Discovery

The integration of Artificial Intelligence (AI) is revolutionising the drug discovery landscape, significantly expediting the traditionally arduous and expensive process.

AI in Target Identification and Validation

AI empowers researchers by swiftly identifying potential drug targets. By analysing vast datasets, AI helps pinpoint molecules and proteins crucial to disease pathways. This precise target identification is a pivotal first step in developing innovative therapies.

AI in Compound Screening and Optimization

AI plays a pivotal role in streamlining compound screening and optimization. With its ability to predict the properties of compounds, AI reduces the need for exhaustive laboratory testing. It identifies promising molecules for further exploration, drastically reducing time and resources.

AI in Antibody Discovery

In antibody discovery, AI shines by analysing data points, antibody sequences, and structures. It assists in designing antibodies with high efficacy against specific antigens, a complex task traditionally. AI also optimises antibody structures, improving affinity and potential therapeutic impact.

AI’s transformative power is underscored by real world examples. Companies like Exscientia and Recursion have AI-discovered drugs in clinical trials for FDA approval. These ground-breaking therapies exemplify AI’s potential to reshape drug discovery and improve the healthcare industry.

AI in Clinical Trials

AI’s role in clinical trials is a game-changer, offering a profound opportunity to reduce the time and costs required to bring a new drug to market. The current process, taking 10-15 years and costing over a billion USD, is largely consumed by expensive clinical trials, many of which end in failure.

AI can revolutionise this landscape by streamlining clinical trials, enhancing participant selection through the analysis of electronic health records and medical literature. It can also boost patient monitoring via digital tracking, provide more accurate disease progression insights using biomarkers, and enhance safety and efficacy data analysis. Furthermore, AI’s data-crunching capabilities can reveal hidden patterns, potentially leading to new treatment targets. While randomised controlled trials remain crucial, AI enables more tailored therapies and trial protocols based on individual patient data.

Artificial Intelligence and Patents: The Challenges

It is paramount to patent AI-related inventions within the pharmaceutical realm, considering the significant investment in research and development.

Patent Eligibility

AI inventions face multiple hurdles before becoming patent eligible. This is due to concerns that such inventions might be perceived as abstract ideas or an existing law of nature, creating uncertainties regarding their patentability.

Enablement Issues

Demonstrating enablement for AI based inventions can be intricate. It may not always be evident that a series of method steps would unfailingly produce the intended outcome, especially when the outcome relies on specific training data or implementation.

Inventorship Concerns

A significant obstacle in patenting AI inventions is determining the rightful owner. The Indian Patent Act in Sections 2 and 6 establishes criteria for identifying inventors and individuals eligible to file patent applications. Section 2(1)(s) defines who can be termed a ‘Person,’ limiting it to ‘natural persons’ and ‘Government Organizations.’ However, experts argue that ‘person’ might encompass other entities as potential inventors. Meanwhile, Section 2(1)(y) outlines criteria for individuals who cannot claim inventorship, but it remains unclear who can be recognized as the actual inventor. 

Presently, Indian and international patent laws outline that inventors must be natural persons to qualify for patents.

Lack of Specific Guidelines

Unlike the European Patent Office (EPO), the Japanese Patent Office (JPO), and the United States Patent & Trademark Office (USPTO), the Indian Patent Office (IPO) lacks distinct guidelines for evaluating AI-based inventions. Additionally, the IPO’s Computer-Related Inventions (CRI) guidelines currently exclude the patentability of AI systems concerning algorithms, mathematical methods, business strategies, and computer programs. This framework may not effectively determine the eligibility of AI-based inventions.

In the case of Microsoft Technology Licensing, LLC v. The Assistant Controller of Patents and Designs [C.A. (COMM.IPD-PAT) 29/2022], decided on May 15, 2023, the court emphasised the need to assess patentability based on the inventor’s contributions and technical effects, rather than just focusing on instruction and algorithm implementation. The court set aside the previous decision, calling for a re-examination of the patent application concerning prior art, inventiveness, and novelty. Furthermore, the court recommended including examples of both patent-eligible and non-eligible inventions in the CRI Guidelines to ensure consistent patent examination.

Securing AI-developed Drug Patents: Strategies and Considerations

To preempt inventorship disputes in AI-based patent applications, it’s vital to highlight the significant human involvement in AI development. This entails shedding light on the human role in defining AI’s mathematical or statistical methods and crafting the datasets for its training. While the use of AI in life sciences helps generate potential drug candidates, substantial human efforts are required for compound synthesis, envisioning practical applications, and devising testing protocols.

Another approach is to reduce AI’s prominence in patent applications and emphasise that AI is just one of several methods for intricate data analysis in drug target identification. By underscoring alternative methods, even less efficient ones, alongside AI, you mitigate the risk of AI being the sole “inventor.”

Effective patent drafting practices involve incorporating human-conceived aspects into applications. These may encompass methods for drug production, usage, pharmaceutical formulations, potential modifications, precursor molecules, and more.

Furthermore, when AI produces specific molecules, expand your claims to encompass a class of molecules defined by human contribution. For instance, if AI identifies molecule A, consider that scientists may classify it into Class I, including molecules B, C, D, and E. This broader claim scope protects against the notion that only molecule A was AI-generated.

Despite potential challenges, investment in AI for drug discovery remains critical. While legal frameworks may evolve, AI’s capacity to significantly reduce drug development expenses and timelines is invaluable. Pharmaceutical companies should persist in their AI-driven pursuits and actively seek patent protection. These strategies offer resilience against AI-generated drug patent disputes and inventorship issues, enabling the pharmaceutical industry to harness AI’s transformative potential without impediments.

In Conclusion

Artificial Intelligence (AI) in the pharmaceutical industry is poised to completely transform drug discovery and clinical trials, offering unprecedented efficiency and effectiveness. Despite challenges in patenting AI-related inventions, proactive strategies and a continued commitment to innovation are essential. AI’s potential to enhance disease understanding, expedite drug development, and its diagnostic precision is a promising path forward. As the legal landscape evolves, it’s imperative for the pharmaceutical sector to harness AI’s transformative power, ensuring a brighter future for healthcare and patient well-being.

Anitha Elizabeth

Patent Attorney

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