Medaffcon strengthens data science team as customer demand grows

Medaffcon in September added two new members to its team of data scientists, preparing itself for an era where pharmaceutical development and innovation are driven increasingly by real-world data.

Medaffcon is expanding its data science team in response to growing demand for expertise in selecting, processing and analysing real-world data to deliver advances in the medical and pharmaceutical care.

“Real-world evidence is getting bigger and bigger and we’re getting more and more research assignments, so we need smart minds and quick fingers to do all the data science,” sums up Iiro Toppila, data analysis lead at Medaffcon.

Real-world data, he believes, are an increasingly important driver of medical and pharmaceutical innovation for multiple reasons: their potential is recognised more widely, the regulatory environment has become less ambiguous, and the tools used to process and analyse them have improved.

“The regulatory environment becoming stricter and more well defined actually makes things easier from one viewpoint: the data may be harder to access and the processes stricter, but the framework is clearer,” he adds.

“But I’d say the bigger thing is that our clients – pharmaceutical and life sciences companies – are finding new ways to use real-world evidence. It’s not just about descriptive studies and about using real-world evidence for the sake real-world evidence; they’re doing things smarter and identifying the needs before finding the tools to answer the burning questions.”

“Not your ordinary data scientists”

Medaffcon strengthened its data science team with two new members in early September, Sanaz Jamalzadeh and Anton Klåvus. The team thereby consists of eight data scientists who each have a background in some aspect of bioinformatics, tells Toppila.

“We’re not generic data scientists,” he states. “We also need to have the context of health and sickness for what’s happening biologically, we need to have discussions with biologists, geneticists and clinical physicians treating the patients. We need to have a common language to understand each other and pull off these real-world data studies.”

Both Klåvus and Jamalzadeh reveal that they were intrigued particularly by the opportunity to leverage their expertise to extract actionable insights from health data, tackle real-world problems identified by medical and pharmaceutical companies, and have a positive impact on the lives of patients.

“We leverage data analysis and machine learning techniques ultimately to support evidence-based decision making in the healthcare and pharmaceutical fields. This has a direct impact on patient lives,” says Jamalzadeh, who for the past five years has been conducting doctoral research at the University of Helsinki.

Her research focuses on developing novel systems biology approaches to identify treatment resistance mechanisms in ovarian cancer patients.

“It’s an interesting setting: you get to do scientific work, but that work is driven by customer need,” concurs Klåvus, who has previously worked with genetics and metabolomics data in both academia and industry.

Medaffcon in September added two new members to its team of data scientists, preparing itself for an era where pharmaceutical development and innovation are driven increasingly by real-world data.

Real-world data drive pharmaceutical advances

Data scientists have a vital role in each customer project at Medaffcon, tells Toppila. It extends from identifying and sourcing the data needed to fulfil the customer-set objectives, to processing and analysing the data, and to reporting the results particularly in regards to the technical aspects of the study – all phases that are carried out in collaboration with in-house scientific advisors.

“Our data scientists have in-depth insight into the different data sources – into what you can and can’t find in them, into their possibilities and limitations,” he says.

Data analysis is becoming increasingly important for pharmaceutical development as real-world data are beginning to be utilised not only to measure the cost and performance of existing drugs, but also in earlier phases of development.

“One emerging hot topic is virtual control arms generated from real-world data,” highlights Toppila. “If you have a small patient population or you can’t have a clinical trial for drugs with a suitable control arm, you can utilise real-world data and patients instead.”

“The distinction between clinical trials and real-world studies is starting to blur in these kinds of settings.”

Jamalzadeh believes the applications of real-world data are bound to increase also as new statistical, machine learning and artificial intelligence approaches enable data scientists to extract more interesting features out of the data.

“There are some interesting opportunities to conduct predictive analyses of hospitalisations and treatment outcomes,” she expounds.

These opportunities are tangible especially in Finland and the Nordics. All countries in the region have comprehensive healthcare systems that generate a constant flow of high-quality data, regulatory processes for accessing the data for research purposes and national identification systems that enable the linking of the data at the individual level.

Klåvus, in fact, argues that such broad-based collection of data induces an obligation to utilise the data as thoroughly as possible to improve outcomes in medical and pharmaceutical care.