Artificial intelligence in the pharmaceutical industry

The world is full of information and the data available to humans today exceeds their processing capacity. There are some market sectors for which data are crucial, one of them being medicine.

Many health researchers say that 80 per cent of scientific publications contain hypotheses that need to be tested and compared with real data, and whether we are talking about new drugs, clinical trial design or drug safety monitoring, artificial intelligence can be a great partner.

Artificial intelligence and drug discovery

The drug discovery process costs an immense amount of time, effort and resources. It takes on average 10 to 15 years and $1.5-2 billion to bring a new drug to market. And even if a given drug goes through all the necessary steps, it may not be a viable drug and the effort to discover it may have been for nothing.  

Today, there is a need to speed up the process and save precious time, which is why pharmaceutical companies can rely on artificial intelligence.

To help researchers identify key data among large amounts of scientific literature, and to scale up drug discovery processes, expert.ai proposes its technology, which is able to:

1. Accurately identify and connect biomedical information (diseases, drugs, treatments, symptoms, genes, proteins),
2. Group conditions and diseases,
3. Identify mechanisms of action and drug classes.

How does it work?

With the scientific literature as the primary source of data on the target's association with disease, expert.ai extracts daily publications and structures drugs, diseases, targets or biomarkers to create a synthesis of knowledge focused on a medical area, enabling rapid insights to uncover causal relationships between targets and diseases, potential treatments and risk factors.
Expert.ai's technology brings a human-like understanding of language to the drug discovery process, enabling researchers to accelerate their research and data analysis. It also supports the identification of potential new applications for existing drugs in an attempt to repurpose treatments in other disease areas.
And the resulting knowledge graph is always consistent and up-to-date with changes frequently occurring within the different therapeutic areas.

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Artificial intelligence and clinical trials

In the medical field, great importance is attached to clinical studies, as by monitoring data and comparing them with previous results, researchers can increasingly improve the quality of future studies.  

Creating a clinical landscape for drug development requires:

1. The acquisition, extraction and linking of data from clinical trials around the world,  
2. The extraction of key points from semi-structured and unstructured data,
3. Organising them in a simple way for use in informed decision-making.

Expert.ai's technology can extract data from more than 700,000 clinical trials worldwide, including clinical trial registries such as clinicaltrials.gov, EUDRA, EUPAS, Japanese registries, Australian registries.

How does it work?

Expert.ai deals precisely with data mapping, deduplication and linking of records to make them easier for researchers to use.

By incorporating NLU and ML technologies combined with standard and customised taxonomies, expert.ai can also understand and link terms such as 'coronavirus', 'COVID-19' and 'SARS-CoV2' so that researchers can identify the data most likely to help them speed up the design and development of their clinical trials.

One of the key criteria for the design and success of clinical trials is related to the recruitment of patients. Inclusion criteria list characteristics such as demographics, clinical status, duration and severity of symptoms. Exclusion criteria list characteristics that could interfere with the study or increase the risk of adverse events. Expert.ai's technology allows these unstructured data to be converted into information and create key attributes for a patient profile.

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Artificial intelligence and side effects

After a drug is approved and an increasing number of patients start using it, side effects may emerge. These can be reported directly by doctors, identified by healthcare organisations or emerge through direct feedback from patients on social media.

Again, artificial intelligence, and expert.ai's technology, can monitor and consequently reduce side effects, helping to improve patient care. It also allows researchers to constantly examine different data sources, including social media, which pose a challenge, as the semantics and syntax used within these platforms are totally different to typical medical language.

How does it work?

Expert.ai is able to leverage its NLU technology and knowledge graph expertise to build a solution that can identify side effects by extracting data from social media. Through the words used by people communicating on social, the format used to structure the posts and the volume of the posts published expert.ai can identify potential concerns about side effects that are not easily accessible and searchable by simple pharmaceutical companies.

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