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and society has not been convinced. As the indus- try attempts to become more patient-centric, so this fundamental trust issue needs to be addressed. In part this will be driven by the delivery of ‘cures’ for some hitherto untreatable and distressing con- ditions, using gene editing and cell therapy, which is stimulating more positive thinking in society and appreciation of the impressive biomedical capabil- ities of the industry. Another hurdle has been the comparatively con-


servative, slow-moving nature of biopharma com- pared to the dynamic and agile nature of digital technology innovation and development. 21st cen- tury Agile development methodologies did not sit well with the constraints of 20th century regula- tions controlling the deployment of computer- based technologies in the regulated domains of the biopharma industry. Today, the industry is more confident in its


recruitment of data scientists. Although the tools with which data science is executed continue to develop, the requirement for biopharma/life sci- ence/healthcare domain knowledge, along with the data analytical and statistical skills, was now accepted as the sine qua non for carrying out the required tasks. During the late 2010s, there had been much dis-


cussion in the pharmaceutical industry about the urgent requirement to recruit data scientists, driven by the need to deploy data analytics on rapidly increasing volumes of RWD. Finding such staff with the requisite skills was problematic – they needed both the technical skills of data analytics along with domain knowledge of the pharmaceuti- cal industry. Table 4 captures some of the skills required in and around 2020; such a view needed to be kept up-to-date as the decade evolved to 2030. In particular, the ongoing need to ensure that staff were sufficiently resourced needed not to be over- looked. This included enabling attendance at con- ferences and similar events so that data scientists could communicate with the wider community and keep abreast of developments. Furthermore, career expectations needed to be managed carefully and understanding specific employee motivations for pursuing data science were key factors in staff retention46.


Regulation In the years leading to 2030, regulatory agencies have been striving to adopt processes, such as adaptive licensing47, that would allow them to get good drugs into the market while continuing their important vigilance to ensure bad drugs did not get approved.


Drug Discovery World Winter 2019/20


For example, in the US it had been predicted that


by 2025 the FDA would be approving 10 to 20 cell and gene therapy products a year. This statistic was based on an assessment of the pipeline and the clin- ical success rates of these products and as such the FDA expanded its workforce including additional Marketing Authorisation Applications reviewers. During the late 2010s, major regulatory agencies


(eg CFDA, FDA, EMA, PDMA) delivered strategy reports embracing the need to support and expedite the development and marketing approval process for many new treatments. For example, the EMA Regulatory Science to 2025 Strategic Reflection48 identified the need for five key objectives:


l Catalysing the integration of science and tech- nology in medicines development. l Driving collaborative evidence generation, improving the scientific quality of evaluations. l Advancing patient-centred access to medicines in partnership with healthcare systems. l Addressing emerging health threats and avail- ability/therapeutic challenges. l Enabling and leveraging research and innovation in regulatory science.


Furthermore, the FDA produced several guide-


lines to help drive clarity in the processes of the deployment of regenerative medicine49 and cellular and gene therapy50 including the longer-term fol- low-up of gene therapy patients. The Chinese FDA also implemented consider-


able change in its regulatory environment designed to reform the administration of clinical trials, accelerate the evaluation and approval process and the promotion of drugs innovation and the devel- opment of generic drugs51. The Japanese Pharmaceuticals and Medical


Devices Agency (PMDA) had similarly been work- ing on its efficiency to ensure new drugs got to market safely and effectively and in a timely man- ner. Indeed, by the mid-2010s the average number of days between an NDA filing and approval of a standard drug in Japan was 306 days, compared to 322 days in the US (FDA) and 366 days in the UK (MHRA). Nonetheless, despite all these efforts, the rate of


change of drug discovery and development and the impact of new, precision-medicine research modal- ities have continued to stretch the resources of the regulatory agencies.


Health Technology Assessment (HTA) For many years, HTAs had been used to help assess the value (and indeed affordability) of therapies.


47


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