Converging global crises are forcing the rapid adoption of disruptive changes in drug discovery

Abstract

Spiralling research costs combined with urgent pressures from the Coronavirus 2019 (COVID-19) pandemic and the consequences of climate disruption are forcing changes in drug discovery. Increasing the predictive power of in vitro human assays and using them earlier in discovery would refocus resources on more successful research strategies and reduce animal studies. Increasing laboratory automation enables effective social distancing for researchers, while allowing integrated data capture from remote laboratory networks. Such disruptive changes would not only enable more cost-effective drug discovery, but could also reduce the overall carbon footprint of discovering new drugs.

3D culture of pancreatic cancer cellsin vitrorecapitulates an aberrant mitochondrial oxidative phosphorylation genotype observedin vivo

“…A more rigorous understanding of efficacy and toxicity at multiple biological levels would then offer a potential solution to this systemic productivity problem. It has been proposed that pivotal decision-making assays, such as those described here, need to be introduced much earlier into the discovery process to enable disruptive changes in drug discovery to make a real difference to productivity [13]. Analytical methods based on decision theory have demonstrated that slight changes in the 'predictive validity' of an assay can have a remarkably significant impact on downstream success rates [11].…”

“…However, overall success rates in pharmaceutical research have been highly unpredictable, especially for the discovery and development of novel targeted cancer therapeutics [11]. However, more predictive pre-clinical screens are now being more effectively implemented into drug discovery workflows to score therapeutic candidates in a way that better correlates with their potential clinical utility [12,13]. It has been argued that the biological and phenotypic complexity of 3D cultures allows for more predictive assays of future clinical efficacy to be developed, as they are grown in a more physiological and/or pathological 3D environment [14].…”

Democratising Automated Compound Screening Strategies in Early Drug Discovery

“…It has been reported that most published research findings are incorrect, which exemplifies the extent to which this reproducibility issue is widespread across many research activities, including drug discovery [7]. It has been proposed that remotely controlled laboratory automation can improve consistency in data quality and that machine learning could enable improved decision making based on the integration of automated data capture in remote laboratories, thus essentially democratising global research networks focused on drug discovery [8].…”

“…Location independence has evolved to reinforce greater organisational plasticity. Analytical methods based on decision theory and implement by machine learning have demonstrated that small changes in the "predictive validity" of an assay have a remarkably significant impact on success rates [8]. The mathematical basis of decision theory is now poised to be implemented through novel algorithms networked together, which is beginning to democratise the drug discovery process away from large, isolated laboratories.…”

“…The mathematical basis of decision theory is now poised to be implemented through novel algorithms networked together, which is beginning to democratise the drug discovery process away from large, isolated laboratories. It is also hoped that these new strategies can potentially reduce the carbon emissions of the he pharmaceutical industry [8], which is thought to be more emission-intensive than the automotive industry [18].…”