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Coronaviruses are a large family of viruses that cause disease in animals and humans. They were discovered as …

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Coronavirus - My Secret Hope

Coronavirus - My Secret Hope

Coronaviruses are a large family of viruses that cause disease in animals and humans. They were discovered as a cause of the common cold mo… read more

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[Interview] The Rise of Quantum Physics in Drug Discovery

[Interview] The Rise of Quantum Physics in Drug Discovery

Computer-aided drug design (CADD) is a central part of so-called “rational drug design”, pioneered in the last century by companies like Vertex. Over the last decades, CADD had great influence on the way new therapeutics are discovered, however, it also showed limitations due to modest accuracy of computational tools.  

The majority of software tools used for computational chemistry and biology rely on molecular mechanics -- a simplified representation of molecules, essentially reducing them down to “balls and sticks”: atoms and bonds between them. In this way it is easier to compute, but accuracy suffers greatly.

In order to gain adequate accuracy, one has to account for the electronic behavior of atoms and molecules, i.e. consider subatomic particles -- electrons and protons. This is what quantum mechanical (QM) methods are all about -- and the theory is not new, dating back to the early decades of the 20th century.  

Becoming Pharma 4.0: How Digital Transformation Is Reshaping Pharmaceuticals

Becoming Pharma 4.0: How Digital Transformation Is Reshaping Pharmaceuticals

The digital transformation of biopharmaceutical manufacturing is continuing at a rapid pace as companies attempt to mine the sources of data available. Innovations include predictive analytics, big data analytics, and creating the digital plant. Digital transformation offers a mechanism to revise its business model, to improve production processes, to design new drugs faster by using artificial intelligence to screen compounds and to increase responsiveness to customers. Furthermore, the volume of data processed by pharmaceutical firms shows no sign of slowing down. This means pharmaceutical companies must act quickly in terms of building core internal digital capabilities and moving beyond their traditional IT functions to all areas of the business.

 

The Evolution Of Pharmaceutical R&D Model

The Evolution Of Pharmaceutical R&D Model

There is a plethora of analytics reports, including ones by Deloitte, DKV Global, and Ernst and Young, all pointing out to a declining business performance of the pharmaceutical industry. They all convey a similar bottomline message: the decline is not due to a lack of innovation (the innovations are growing). And not because sales are falling or markets are shrinking (revenues are growing in general, and the markets are expanding with the expanding and ageing population). The key reason of the declining financial performance is the fact that research and development (R&D) costs are growing substantially faster over an average investment period, than the actual revenues over the same period. This kills operational profits, leading to a decline in the overall business gain. A direct consequence of that -- an increasingly stagnating industry, cutting sometimes promising R&D programs, jobs etc.  

There are two more relevant questions here: 

1) why R&D costs are growing faster than revenues, considering that technological progress is seemingly providing more and more optimal and powerful technologies to pharma companies at a constantly decreasing specific price (e.g. costs of computation, sequencing, screening and many other things are falling), and 

2) what to do about it to reverse the decline in pharma industry performance? 

Interviews with experts

[Interview] The Rise of Quantum Physics in Drug Discovery

[Interview] The Rise of Quantum Physics in Drug Discovery

Computer-aided drug design (CADD) is a central part of so-called “rational drug design”, pioneered in the last century by companies like Vertex. Over the last decades, CADD had great influence on the way new therapeutics are discovered, however, it also showed limitations due to modest accuracy of computational tools.  

The majority of software tools used for computational chemistry and biology rely on molecular mechanics -- a simplified representation of molecules, essentially reducing them down to “balls and sticks”: atoms and bonds between them. In this way it is easier to compute, but accuracy suffers greatly.

In order to gain adequate accuracy, one has to account for the electronic behavior of atoms and molecules, i.e. consider subatomic particles -- electrons and protons. This is what quantum mechanical (QM) methods are all about -- and the theory is not new, dating back to the early decades of the 20th century.  

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

[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

[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.

Editor's Pick: A Video Blog

How AI Is Applied To Metabolomics To Identify New Targets And Drugs

   by Andrii Buvailo    4091
How AI Is Applied To Metabolomics To Identify New Targets And Drugs

The team at MIT created the most comprehensive database of metabolites, their interactions with proteins, protein-protein interactions, drug-protein interactions, and associations of metabolites with diseases. They then use the obtained interactions map to make inferences about the disease mechanisms and novel targets. With this new technology, the team launched a biotech startup ReviveMed in 2016 having raised $1.5M of funding so far.

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Network Driven Drug Discovery Using AI

   by Andrii Buvailo    3416
Network Driven Drug Discovery Using AI

“One-target-one-disease” paradigm has been around for decades, prompting numerous drug discovery programs focusing on identifying small molecules for targeting only one specific protein (or other targets) believed to be responsible for the disease mechanism.

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Evaxion Biotech Uses AI To Develop Anticancer And Antimicrobial Vaccines

   by BiopharmaTrend    3507
Evaxion Biotech Uses AI To Develop Anticancer And Antimicrobial Vaccines

This is an interview with Niels Iversen Møller, a CEO, and Co-founder of Evaxion Biotech -- an immuno-informatics biotech from Denmark.

Evaxion Biotech uses its machine learning-based platform to compare DNAs of tumor cells and DNAs healthy cells and identify mutations that are critical for the disease. This data is further used to design anticancer vaccines.

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