A Landscape of Artificial Intelligence (AI) In Pharmaceutical R&D

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Biopharma Insights


[Interview] Applying AI To Shape Business Strategies At European Pharma Organizations

   by Andrii Buvailo    1101
[Interview] Applying AI To Shape Business Strategies At European Pharma Organizations

The application of artificial intelligence (AI) in the pharmaceutical industry has become a long-term strategic priority for most companies. However, the efficiency of this endeavor depends greatly on the availability of large volumes of properly curated quality data, which is not always the case.

While pharma organizations generate huge volumes of data across all stages of drug discovery, development, and commercialization, not all types of data are equally useful for building efficient machine learning (ML) pipelines. For instance, it is relatively easier to apply AI-tech to consumer-related business processes, where lots of well-understood and properly labeled data is available, than it is for basic research tasks, where data is complex, often poorly labeled and extremely domain-specific.

The above situation leads to a faster pace of progress with AI application in such areas as financial analysis, consumer-behavior prediction, patient classification, marketing, and so on.

One of the important hurdles that pharma companies are trying to solve using AI tech is brand management. Indeed, understanding peculiar features of various patient categories, their purchasing behaviors, reactions to different products, revealing possible risks and side-effects for each class -- those things become essential for pharma companies to be able to develop and implement truly patient-centric brand management strategies. Luckily, this is one of the most fruitful areas for the application of machine learning (especially deep learning) models.

To get a better understanding of how it can be done, I have asked several questions to Agnieszka Wolk, Senior Director, Data Science, IQVIA, who recently presented this topic at the PMSA 2019 European Summit in Basel, Switzerland. . 

 

[Interview] Demystifying the Role of Artificial Intelligence in The Life Sciences

   by Andrii Buvailo    1166
[Interview]  Demystifying the Role of Artificial Intelligence in The Life Sciences

In this interview, Rasim Shah, Director at OKRA Technologies provided a glimpse into how the company applies state of the art machine learning technologies to solve real world challanges in the life sciences. Rasim also agreed to answer several questions about a more general context of AI in pharma, its current challanges and future perspective, as well as describe the current efforts the European Union puts into supporting the AI ecosystem in the region.

 

Rasim Shah, Director at OKRA Technologies:

OKRA Technologies is a leading European artificial intelligence (AI) company for life sciences. Our goal is to empower life science executives at their desks or whilst on the move, with explainable AI outputs. OKRA’s solutions deliver suggestions, predictions and explanations to enable life sciences executives and operational teams to drive the right drug to the right patient with humanised and understandable AI outputs. The OKRA engine learns from real-world data, structured, unstructured, clinical, commercial and scientific literature to drive the right insight to the different teams in life sciences. Our deep expertise in AI, combined with in-depth medical and product knowledge from life science leaders, has allowed us to develop and co-create products that can transform the way life sciences approach traditional industry challenges. We focus on operationalising AI in an ethical way by placing users of these systems at the centre.

Top AI in Pharma and Healthcare Conferences in 2020 You Can’t Miss

   by Andrii Buvailo    20912
Top AI in Pharma and Healthcare Conferences in 2020 You Can’t Miss

Artificial intelligence (machine learning and deep learning, to be more specific) has become widely discussed topics in the area of life sciences and healthcare over the last several years and the excitement keeps growing. While a lot of pharmaceutical companies and healthcare organizations express considerable interest in possible new opportunities, associated with the use of artificial intelligence for early drug discovery, clinical trial optimization, and business intelligence, a considerable gap still exists when it comes to understanding new technologies by pharmaceutical professionals and leaders. The key questions here are these:

  • What machine learning / AI can and can’t do for the pharmaceutical industry

  • What should be done to harness practical and measurable value out of machine learning / AI?

  • How it should be done and what are the timelines for getting returns on investments? 

Is Pharma Ready For Serialization? The Answer Lies In Digital Technology

   by Tim Sandle    1469
Is Pharma Ready For Serialization? The Answer Lies In Digital Technology

New legislation requiring pharmaceutical companies to implement 'serialization' is now coming into force. This means that no counterfeit product should enter the supply chain and no legitimate product is diverted from its intended destination. To work effectively, serialization requires a comprehensive system to track and trace the passage of prescription drugs through the entire supply chain. The application of track and trace principles can help to avoid counterfeit medicines from entering the supply chain. To be effective, digital technologies such as blockchain and RFID-enabled tag and trace systems need to be embraced.

Choosing The Right CRO For Pharma R&D Outsourcing

   by Henk Johan Streefkerk    797
Choosing The Right CRO For Pharma R&D Outsourcing

This post was originally published on Quora, answering the question: "How do drug manufacturers select the right contract research organization(CRO)?"

Selecting the right contract research organization (CRO ) obviously depends on what activity is outsourced and with which regulations the outsourced processes need to comply. However, the following criteria certainly apply to pharma companies.

Typically, we would request evidence on how often the CRO has performed the activity, i.e., what their relevant experience is from a technical operation point of view. However, it is also very important to understand how they deal with Quality Assurance, Training, Disasters, and if they have been inspected by health authorities before, if upon inspection there were any critical findings, and how they managed these findings.