Biotech - What AI is really doing for the industry

Unveiling Efficiency, Accuracy, and Speed

Common issues biotechs face concerning AI

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Data Security

Life science partners with AI struggle to shield crucial data from cyber threats, risking patient confidentiality and research integrity.

Regulatory Tightrope

Navigating AI innovation while meeting strict regulations challenges biotechs, requiring a delicate dance to ensure compliance without stifling progress.

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Regulatory Tightrope

Navigating AI innovation while meeting strict regulations challenges biotechs, requiring a delicate dance to ensure compliance without stifling progress.

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AI Complexity Hitch

Understanding intricate AI algorithms proves elusive for biotech pros, casting shadows on transparency and hindering trust in decision-making.

Integration Juggle

Merging AI seamlessly into existing biotech setups becomes a battleground, demanding compatibility, scalability, and minimal disruption.

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Integration Juggle

Merging AI seamlessly into existing biotech setups becomes a battleground, demanding compatibility, scalability, and minimal disruption.

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Artificial Intelligence, or ‘AI’, is revolutionising the biotech sector by bringing a new level of efficiency, accuracy, and speed to the industry. From drug discovery to clinical trials and precision medicine, AI is playing a significant role in shaping the future of biotech.

Let’s take a look at the biotech/AI relationship and see how AI is helping and hindering the industry.

Funding Innovation

Drug Discovery

The process of drug discovery has traditionally been long and arduous, taking several years and vast amounts of resources — such as securing the right funding through a biotech VC — to bring a single drug to market. AI is changing that by leveraging machine learning algorithms to analyse vast amounts of data and identify new targets for drug discovery. 

This not only speeds up the process for R&D directors but also makes it more cost-effective. AI algorithms can analyse chemical and biological data to identify new drug candidates, predict their efficacy and toxicity, and streamline the preclinical testing process.

Clinical Trials

Clinical trials are an essential part of the drug development process and determining the safety and efficacy of new drugs. However, traditional methods can be time-consuming and costly. 

‘AI is streamlining the process by predicting patient outcomes,’ says Roop Chandwani, CEO of Mazards, a retained biotech executive search firm in London. ‘It reduces the number of patients needed for trials, and increases the efficiency of trial design and execution.’ 

AI can also help identify the best candidates for a trial, optimise dosing, and minimise the risk of adverse events. This has come especially beneficial during the Russian-Ukrainian conflict as many trial patients that would typically come from Russia but are no longer able to because of the conflict. 

Precision Medicine

Precision medicine is a rapidly growing field that seeks to personalise medical treatments based on individual patient characteristics such as genetic data, lifestyle, and medical history. AI is playing a crucial role in this field by analysing vast amounts of data to identify patterns and predict patient outcomes. 

This enables healthcare providers to develop more personalised treatment plans and improve patient outcomes. AI algorithms can also help identify the best therapeutic options for individual patients, leading to improved treatment success rates and reduced side effects.

Cost Reductions

One of the greatest problems in biotech is cost—the cost to innovate which directly affects the cost in the marketplace. AI advancements seek to reduce these costs wherever possible, directly linking AI to drug innovation itself—especially drug prices that are rising above average global inflation rates. 

There has always been a questionable percentage of markup on big pharma products. Part of that markup is dependent on the ever-increasing costs of innovation. With new inflation challenges, are inflation rates sustainable to put on top of already expensive health therapies? 

And so cost reduction is priority number one in reestablishing balance for life science companies. If AI can successfully reduce the costs of innovation, the drug pricing problems will be relieved.  

In 2021, Novartis launched Novartis Digital Health, a new arm seeking to transform the life sciences by lowering the costs of innovation and development. The reductions will decrease bankruptcies related to drug-cost and lower insurance premiums, especially for patients in the US. 

Piers Morgan, Chief Corporate Development Officer of Pangea Botanica, a research company at the forefront of nature-inspired biotech, also believes that AI will relieve the cost of innovation. ‘AI is the most exciting field to watch, specifically, the technologies that can convert data and demonstrate value.’  

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