Generative AI in Pharma and Biotech

According to the glossary of AI terms, Generative AI is a subset of Artificial Intelligence. It’s built on large language models (LLMs), which are algorithms trained to generate relevant content by “learning” information from millions of pre-existing examples.

When a user submits a query to a Generative AI tool, the algorithm uses what it knows to construct a personalized response. It’s essentially an advanced form of predictive text. The result is a humanlike response as opposed to something that looks like it was generated by a robot.

Although Generative AI tools are often used to get simple answers, detailed and specific queries can generate longer and more thorough responses.

Utilizing deep learning algorithms and neural networks, Generative AI, a sophisticated class of Large Language Models, has the remarkable capacity to generate fresh, original content. Originating from relatively rudimentary applications in the 1950s, it has undergone a significant evolution. Owing to the exponential growth in data availability and computational power, its applications have dramatically diversified and complexified.

The McKinsey Global Institute estimates that this technology could generate between $60 billion and $110 billion annually in economic value for the pharmaceutical and medical products industries.

Generative AI fits seamlessly into pharmaceutical companies by enhancing key stages of the drug development process. Its capabilities in data analysis and pattern recognition allow for more efficient screening of vast chemical libraries, pinpointing compounds with potential therapeutic effects more rapidly than traditional methods.

Generative AI supports the design phase, where it can propose novel drug molecules and predict their interactions with biological targets, thus accelerating the transition from conceptual frameworks to preclinical testing.

Additionally, Its capabilities exemplify the power and potential of Generative AI in transforming communication and user experiences for customer support purposes.

By integrating Generative AI, pharma companies can not only expedite the discovery and development of new drugs but also improve the precision and reduce the costs associated with bringing effective treatments to market with an AI-enhanced customer experience.

Some biotech companies are already using Artificial Intelligence in their quest to find new drugs and Generative AI platforms create a more user-friendly interface between workers and their discovery systems. These systems are often complicated and require endless clicking and reading to find information.

By leveraging Generative AI to search the discovery system’s database, information is gathered and presented to the user in a simpler, easy-to-read manner. Drug hunters can especially benefit from a Generative AI simply because of the sheer amount of information they need to sift through to do their job.

For instance, when looking at graphs that show genes that interact with certain substances, engaging a conversational AI makes their task easier and more enjoyable. They can ask the Gen AI platform how many genes are in a particular graph, find associations between genes and diseases faster, provide information, and ask even more detailed questions to get information instantly.

AI-assisted drug discovery shows the potential to be more efficient, predictable, and cost-effective than the traditional model. The key to making Generative AI work in this industry lies with training, and so far, some drug discovery companies have made it work well.

In fact, one company has used an Artificial Intelligence model to design proteins that kill E. coli bacteria. The amino acid sequences differ from natural proteins, but they fold in the same way. Here’s a deeper look at some of the best use cases for Generative AI in the biotech and pharmaceutical industries:

Typically, creating new drugs is a time-intensive process that requires testing millions of compounds. Generative AI models can help drug hunters create the exact molecules they need much faster.

For instance, if they need a molecule that is easily absorbed, Generative AI can help them find it quickly. Gen AI also has the potential to help drug hunters make existing molecules safer and more effective or create entirely new compounds.

We’ve seen many times where AI is used to create drugs that are being tested in the trial stage. But this is the first time a finished medicine made by AI has been reported by Clinical Trial Arena.

The exciting news is, that InSilico Medicine is a company in Hong Kong that uses AI. They have used this technology to make a new drug called INS018-055. This drug is for treating a lung disease called idiopathic pulmonary fibrosis (IPF).

Provide Fast Access to Drug Information

Physicians get information about new and existing drugs from various sources, including their colleagues, journals, direct mail, pharmaceutical reps, hospital clinical meetings, and lectures. Reliability aside, all of these sources take time to access.

With Gen AI, a physician could type a simple prompt asking for information about a drug, its indications, and contraindications, and ask if it’s been black-boxed. The chatbot would return the relevant information almost immediately.

Provide Easy Access to Clinical Studies & Patient Data

Clinical studies play an important role in how a doctor prescribes medication to patients. Doctors need to know a drug is relatively safe – along with potential side effects – before prescribing it to anyone. Sometimes doctors discover new drugs because they come across a clinical trial.

The problem is that doctors are on their own to research clinical trials. If they don’t know the name of a drug, they have to search for the clinical trial data or trials conducted based on symptoms, specific illnesses, and other phrases.

There are online search tools run by organizations like the National Institutes of Health (NIH), but they’re not powered by Generative AI. This means physicians need to know exactly what keywords to type in to get the results they want.

With a Generative AI chatbot, they can ask natural questions and get information without knowing every single related keyword.

Drug Optimization

When a drug candidate is found, Generative AI can be used to make similar molecules with more desirable properties.

Repurposing existing drugs

Sometimes existing drugs have potential uses that haven’t been explored yet. Generative AI can help drug hunters identify potential ways to use these drugs to treat additional health issues.

Personalized Medicine

Personalization is something seen in just about every industry, and it also has a place in the biotech and pharmaceutical industries. Personalized treatments can be created using Generative AI a similar AI models.

For example, if there is a group of people who need a specific effect, Generative AI can assist in identifying or creating a special molecule to serve that purpose. There is also potential for creating molecules on an individual basis for patients who need side effects to be eliminated.

Clinical Trial Testing

During clinical trials, new drugs are tested for safety and efficacy prior to being approved for use by the FDA. Generative AI can increase the efficiency of these trials by finding groups of people who are more likely to respond to the drug.

For instance, Generative AI can find groups with specific genetic markers that predict how people might respond to the new drug. Finding people who will respond favorably will help the trial prove the drug works and should be on the market.

Support the Public’s Need for Information

Many people research drugs on their own, so having a Generative AI chatbot available to the public would greatly improve the patient experience. Hospitals and physicians could offer this kind of tool on their website for patients and drug companies could do the same.

Patient Support Services

• Care Management: Provides comprehensive patient support with the power of AI Customer Care, including instructions on drug administration, managing side effects, and guidance on using specific medical devices.
• Clinical Support: Keeps the clinical support team informed with the latest product updates and FAQs to effectively address patient questions and concerns.

Operational Efficiency

• Technical Support Inquiries: Offers assistance to medical staff with IT and interdepartmental requests, such as setting up new accounts, resolving connectivity issues, and updating benefits or profile information with the power of AI Service Desk.
• Appointment Scheduling and Reminders: Manages scheduling of appointments and sends proactive notifications about upcoming therapy sessions and prescription refills.

Financial and Feedback Services

• Patient Financial Aid Assistance: Provides patients with information on financial assistance programs, including eligibility and enrollment criteria is.
• Customer Feedback Management: Gathers confidential patient feedback and responses through multiple channels, including in-app surveys and feedback forms.

Aside from the possibility of error, there are some challenges to using Generative AI in the biotech and pharmaceutical industries. For instance, Generative AI doesn’t exactly work out of the box.

Technically, anyone can type into a chat box and receive a response, but that doesn’t necessarily provide usable results. To get value from this technology, prompts need to be carefully and intentionally crafted to elicit optimal responses.

Some basic challenges include:

Data quality. Generative AI only works because it uses existing data as training. If the input is poor, the output will be poor and might generate poor models and inaccurate predictions.

Complexity. Getting approvals in these industries is a requirement, but since Generative AI is hard to explain, this could create roadblocks. At least until a few companies create an explanation that can be used as a general template for others.

Misuse. Although personalized drugs are a good thing (for those people), they could become controversial if too many people are excluded. For instance, a drug created to treat a specific disease should ideally work for everyone, but Generative AI might be used to make such a drug that only works for people with certain genetic markers.

Privacy. At some point, if any patient information is entered into a prompt, this could create a legal problem under HIPAA and other data privacy regulations. Private health information must be protected at all times, and whether a Generative AI platform processes or stores prompts, the entire web server and all software involved will need to be HIPAA-compliant.

Another potential business challenge is getting access to a Generative AI system that has access to the right database of information. This can be costly to create on your own, but thankfully, there are companies creating Generative AI systems specifically to serve the biotech and pharmaceutical industries.

By now, the benefits of using Generative AI, like AiseraGPT, in the biotech and pharmaceutical industries should be clear. If you haven’t checked out what this technology can do for your company, now is the perfect time to find out.

If you’re ready to take your pharma business to the next level and provide personalized care, get started with Generative AI to support your staff and customers. Generative AI platforms transform the pharma and biotech sectors. Book a custom AI demo to experience the power of Generative AI today!