‘Digital technologies can pave the way’: Modernizing drug development

By Maggie Lynch

- Last updated on GMT

‘Digital technologies can pave the way’: Modernizing drug development

Related tags Drug development process Regulation Artificial intelligence mHealth Wearables

Drug development – a dynamic and evolving process – is modernizing at an unprecedented rate, says industry expert, who stresses that change must continue to fully explore the opportunities.

The addition of wearable technology​, electronic data management​, and artificial intelligence​ has modernized the drug development industry. However, changes must go hand-in-hand with regulatory missions to be successful.

To further discuss the industry’s evolution, Outsourcing-pharma (OSP) spoke with Pia Windelov (PW), the director of product strategy at Lionbridge Life Sciences with 15 years in R&D in the pharmaceutical and contract research organization (CRO) sector, about the ways in which the industry has modernized – and the way in which it can continue to do so.

OSP: What are some key examples of how the drug development process has been modernized?

PW:​ The drug development process has always been dynamic and evolving due to several factors: technological innovation, discovery of new diseases, changes in disease prevalence, global regulatory harmonization, and not least, the migration from a cumbersome paper-based documentation practice to electronic capture of clinical data.

Although drug development is in constant flux, it is a complicated endeavor to develop a drug which is both efficacious and safe, and to generate the confirmatory and statistically sound data required for regulatory approval.

Due to the complexities, investments, risks, and regulatory requirements involved in pharmaceutical drug development, the industry tends to lag behind other industries in adopting new digital platforms.

Having said that, it seems that modernization is happening in this area at an unprecedented pace. Some of this is clearly due to the industry and regulators having discovered the benefits of digital platforms in drug development and health care.

A good example is the use of mobile health (mHealth) and wearables, which are modernizing and facilitating the execution of clinical trials. In addition, they are improving data quality and patient adherence.

The benefits are obvious of using mobile devices to capture electronic patient reported outcomes (ePROs) or to perform disease monitoring, vital sign measurements, or other study procedures from the patient’s own home setting.

Not only do these mobile platforms enable accurate data in real-time, and treatment adherence, they also mean convenience to trial participants and patients who can reduce time spent on clinic visits and travel. I believe we will see virtual trials growing and modernizing drug development; especially in developed countries where computer literacy and access to mobile devices is high.

OSP: What have been the most significant changes to the drug development process? What needs to continue to change?

PW:​ The push for transparency and ethical disclosure of research results and product performance, which is driven by patient organizations, industry associations, as well as regulators.

The new clinical trial regulation in EU will make plain language summaries of clinical trial results a legal requirement in EU, and we see that communication aimed at the public and patients in nontechnical everyday language is increasingly becoming an industry commitment.

The emergence of artificial intelligence (AI) in drug development – although these are early days to fully understand the impact – is expected to accelerate the development of drugs and time to market by acquiring and integrating data from multiple sources, such as clinical data, lab data, social media, and wearables. Also, it may become instrumental in realizing patient enrollment targets by analyzing historical clinical data and identifying eligible patients based on advanced AI modeling.

What needs to continue to change? Well, change itself needs to continue to fully explore the opportunities that digital modernization can bring to healthcare and drug development.

OSP: What are ways to build a framework to match the evolving research industry?

PW:​ Communication and technology are vehicles to clinical trial success and product realization. Accuracy in local language content is critical to the safe use of a drug or device once it travels into the hands of the end-user, whether it is a patient, a physician, or a laboratory operator.

Virtual trials, mHealth, and transparency requirements generate the need for more patient-facing communication as well as education of health care professionals. Social media listening can help our customers tap into the conversations going on in the everyday near-context of their patients and on the impact of their products on quality-of-life. This can aid in the development of stronger trial designs that focus on the patient.

Also, the importance of linguistic accuracy, health literacy, and migration of content across various digital platforms will only increase.

Building a framework that meets evolution in drug development is not easy but the recipe for success certainly includes a triangle of listening to customers, understanding regulations, and enabling an effective global digital communication.

OSP: What are some products that can be developed to enable stronger trial design and protocol for modern clinical trials?

PW:​ Digital technologies can pave the way to stronger trial designs and can unlock great potential in drug development and patient safety. Real world data (RWD) and real world evidence (RWE) help scientists understand diseases and effectiveness of existing treatments used in real practice.

The knowledge achieved from big data sets can help design clinical trials that reflect real-world context. Also, stronger trial designs can be obtained via electronic data capture of patient-reported data and utilizing the benefits of wearables and mobile devices.

OSP: Why have some members of the industry slower to adopt new approaches to clinical trials?

PW:​ I don’t think it is about reluctance as much as it is a question about the risks, costs, and the operational challenges in changing approach.

Due to the scrutiny given to data integrity in drug development, new innovative ways of handling clinical data or trial designs may trigger extensive procedural changes, validation requirements, investments, new suppliers, and training of all stakeholders down the study execution chain.

OSP: What can regulatory agencies do to continue to modernize the research industry?

PW:​ Regulatory agencies need to follow the technological innovation and provide guidance to the industry that will facilitate adaptation and acceptance of data generated via new digital platforms. This is already happening although I think it will take time before the full benefits and management of especially AI and RWE are known.

Regulatory authorities carry a heavy responsibility of protecting the wellbeing of trial participants and human health, and the way they do this is to ensure data integrity, statistically sound scientific research, and systematic drug monitoring in the development and lifecycle management of drug products. Modernization has to go hand-in-hand with these regulatory missions to be successful.

Related news

Show more

Related products

show more

Saama accelerates data review processes

Saama accelerates data review processes

Content provided by Saama | 25-Mar-2024 | Infographic

In this new infographic, learn how Saama accelerates data review processes. Only Saama has AI/ML models trained for life sciences on over 300 million data...

More Data, More Insights, More Progress

More Data, More Insights, More Progress

Content provided by Saama | 04-Mar-2024 | Case Study

The sponsor’s clinical development team needed a flexible solution to quickly visualize patient and site data in a single location

Using Define-XML to build more efficient studies

Using Define-XML to build more efficient studies

Content provided by Formedix | 14-Nov-2023 | White Paper

It is commonly thought that Define-XML is simply a dataset descriptor: a way to document what datasets look like, including the names and labels of datasets...

Related suppliers

Follow us


View more