The term ‘drug development’ covers a wide range of activities, from the creation of new compounds to clinical trials, all of which help drive a particular medicinal product to market.
In its entirety, the process can take more than a decade, costing pharmaceutical companies millions of dollars, with the vast majority of compounds proving unsuccessful.
Additionally, very few compounds tested during the drug development process make it to market to be used by patients. Nathalie Maubon, Pharma IQ advisory board member and founder of HCS Pharma, says: “At the moment, only one out of nine drugs entering the clinical trial phase goes to market.”
She adds: “Productivity in terms of finding new effective treatments has never been so low. There is a need to change the whole process, with more predictive in-vitro models to better predict the success of a drug in later clinical trials.”
Improving speed-to-market in drug development
Time-to-market is becoming increasingly important due to the growing need to reduce turnaround times. A good example of this was the production of Covid-19 vaccines at the peak of the pandemic which made any systems or technologies that increase the speed of the process highly valuable.
Adopting digital tools in the lab such as shifting from paper systems to cloud-based platforms, automating processes and using predictive algorithms, are all helping scientists work more efficiently.
Artificial intelligence (AI) in particular, has emerged as a key tool to enable the analysis of huge data sets, speeding up research and therefore bringing new drugs to market faster.
Matt Truppo, global head of research platforms at Sanofi, says: “AI enables us to have insights that we could not otherwise gain just from using our own brain power because the datasets are just too vast.”
Truppo adds: “When you start to think about the data in that way and how can you more precisely identify the target you should go after, that is how you are able to accelerate time to market and also gain a higher probability of success in the clinic – this ultimately leads to increased productivity.”
Understanding the stages of drug development
At the beginning of the drug development process scientists identify targets associated with the disease being treated, along with the compounds they believe will influence these. The target is then prioritised and validated based on the ability of the compound to affect the cells.
After target validation the next stage is assay development, when the activity of the compound is measured in an organism or organic sample.
A lead compound – one which is thought to have the potential to treat the disease – is then identified. It can be compared with existing drugs to establish if they are likely to be successful. During the lead optimization stage, scientists identify the compound that will be most effective.
Christos Nicolaou, director in digital chemistry at Recursion Pharmaceuticals, explains how advanced technologies like automation are helping scientists improve these drug discovery processes.
“Currently it takes months from the time a molecule is designed, typically in silico, to send it off for synthesis where expert synthetic chemists will have to come up with a route, order the reagents and perform the reaction before purifying it. Then they have to make sure the synthesis effort worked before passing it on for testing,” he explains. “Imagine having all these done under one roof executed by an automated platform – the goal is to go from months to weeks to days.”
Once these stages have been completed, the compounds will then move on to the clinical trial phase.
How clinical trials impact speed of drug development
Phase I clinical trials are the first stage where the products are tested on human subjects to establish the safety of the drug, generally involving a small number of healthy volunteers. Phase II trials take place once the initial safety of the study drug has been confirmed, and are performed on larger groups of people to assess how well the drug works.
Phase III studies are the definitive assessment of how effective the drug is. They are the most expensive, time-consuming and difficult trials to design and run, as they require a larger group of participants and have a longer duration compared to Phase I and II trials. From here the drug must gain approval from the required regulatory authority before commercialization.
Decentralized clinical trials allow participants to take part in studies from home. These have emerged as a way to improve the recruitment and retention of participants, as well as help expand the pool of participants. For example people with mobility issues or who normally would not wish to travel to a clinical trial site may find it easier to take part.
Decentralized trials are enabled through the use of data-capturing apps and wearable devices to gather clinical data, along with software to analyze this data. Overall, designing the trials with consideration for patient requirements is a key to improving success rates.
Why making drug development more efficient is vital for public health
The most studied disease in clinical trials is cancer, with US$6.5bn spent on trials investigating oncological treatments in 2020. As an example, Bristol Myers Squibb alone is studying the effectiveness of more than a dozen compounds to treat different forms of cancer. It remains a leading cause of death and in 2020 was responsible for nearly one in six deaths around the world, hence the search for a cure remains a big area of focus.
This is followed by US$2.6bn spent on researching HIV/AIDS treatments and more than US$1.3bn on Alzheimer’s disease in the same year. Dementia, of which Alzheimer’s is the most common form, currently affects 55 million people around the world, however, this figure is expected to rise to 139 million by 2050. In 2019 the estimated total global societal cost of dementia was US$1.3tn, and experts warn that costs will become unsustainable for health systems if reliable treatments are not found.
Meanwhile drug development is becoming more complex, particularly due to the demand for biologics such as vaccines and cell and gene therapies, that are created from living organisms. However, AI, machine learning (ML) and modern techniques that recreate living cells are transforming the process of drug development, says Maubon.
“We are at a turning point in biotechnology,” she notes. “Breakthrough technologies are now sufficiently mature to be used to find new treatments, replace deficient cells and in the future, recreate organs from patient cells.
“With new tools like deep/machine learning, AI, connected objects and virtual and augmented reality, the health and wellbeing of humans will evolve in the near future,” Maubon adds.
Quick links
- Why digital transformation is essential to get value from R&D data
- How to improve manufacturing process efficiency to increase speed-to-market
- The benefits of remote monitoring for sample storage and lab processes
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- This article was originally published on November 10th, 2010 and updated on November 18th, 2022.