This transformation is especially necessary in a sector recognized as one of the most inefficient globally due to the high costs and long times involved in the development of new molecules, where the investment of time, money and data in research does not always produce positive results.
According to the World Economic Forumapproximately 90% of pharmaceutical projects fail in clinical trials, generating vast amounts of untapped resources and data. This is where AI can step in, not only to reduce the cost overruns on new products, but also to boost efficiency and innovation in the industry.
What is the current state of the pharmaceutical industry?
The pharmaceutical industry The US faces several structural challenges that limit its ability to innovate effectively. First, there is a significant loss of resources in research and development (R&D) due to the lack of efficacy or toxicity of drugs in development, which do not work as expected or turn out to be too toxic.
In addition, pharmaceutical companies must contend with the short life span of patents, which can reduce the incentive to invest in long-term projects.
In this context, the artificial intelligence It emerges as a promising solution to optimize R&D processes, allowing companies to identify the most suitable therapeutic targets and develop optimized molecules with greater precision and fewer resources.
Adopting generative AI can help pharmaceutical companies conduct parallel clinical trials for the same drug across multiple indications, increasing the likelihood of success and reducing financial risk.
What is the potential of AI in global pharmaceuticals?
Globally, AI is being used to develop new molecules and improve drug discovery processes based on molecular dynamics simulations. Companies such as Googlewhich launched its third version this year AI AlphaFold [2] for modeling complex proteins, and NVIDIA [3]with its biological modeling platform Bionemoare leading these advances.
In the field of language systems, Google is working on Med-Gemini [5]a specialized model to assist health personnel and patients, while OpenAI (ChatGPT) supports projects such as Lifespan [4] oriented to the same field. This approach not only improves efficiency in pharmaceutical research, but also reduces the time needed to bring a new drug to market.
In addition, generative AI can track key management decisions and researcher performance from the start to the end of a program, allowing for greater transparency in the R&D process.
AI platforms trained on pharmaceutical data can function as multi-agent systems, helping companies better manage hiring, executive performance, and business development, licensing, and acquisition decisions.
AI in the Latin American pharmaceutical industry: a tailored approach
However, while global expectations of AI in the pharmaceutical industry focus on the development of new molecules [6] [7] [8]for our region, Latin America, this approach may not be the most appropriate. pharmaceutical industry In countries like Argentina, it is not focused on the discovery of new molecules, but on the improvement of formulations and pharmaceutical and biotechnological processes related to existing generic or bioequivalent drugs.
Therefore, Investment in AI-based solutions should focus on areas where we can generate greater local impact and where we can have some competitive advantage. In this context, machine learning and generative AI applied to a business environment can play a crucial role in optimizing production and document processes to improve operational efficiency.
For example, a Generative AIspecially trained for the pharmaceutical sector, could increase the capabilities of professionals in tasks such as the design of production facilities, investigation of deviations, regulatory compliance, document writing, training, internal mail, customer support and marketing activities. In these cases, the formation of a man-machine team is required to increase the productivity and quality of the work performed.
Other use cases involve using machine learning to analyze large volumes of process data to generate digital twins that allow for optimization, failure predictions, real-time product quality control, or even predictive alerts to change course before generating a possible impact on the product.
In this way, AI becomes a strategic partner to automate manufacturing processesimproving product quality and reducing human errors, which is essential to comply with international regulatory standards and improve competitiveness in the global market. This, however, requires technological partners who understand internal processes and can generate solutions tailored to the needs of the industry.
Towards AI adoption in Latin America
AI represents a unique opportunity to transform the pharmaceutical industry at a global and regional level. While in the most advanced markets AI is driving the development of new molecules, in Latin America its greatest value lies in the optimization of existing pharmaceutical and biotechnological processes. Investing in integrating AI into your processes will not only improve operational efficiency, but will also enable local companies to compete in an increasingly competitive global market with a high level of regulatory demands.
As the pharmaceutical industry As these technologies are adopted more widely, we will see significant improvements in efficiency and increased quality. However, it is essential that this adoption is strategic and tailored to the specific realities and needs of each region.
For Latin America, this means focusing on the potential of AI to improve product quality, regulatory compliance and boost the capacity of its professionals.
The key lies in an intelligent and adaptive adoption of AI technologies that includes people to make the most of their resources and capabilities, thus driving the development of a more efficient and innovative pharmaceutical industry.
Founder of Pharma.IA, MIT Certified CDO, Master in Technological Innovation Engineering UNIBO.
Source: Ambito
