Big Data in Pharmaceutical R&D: Creating a Sustainable R&D Engine

Evolution of Drug Development and Regulatory Affairs: The Demonstrated Power of Artificial Intelligence

PURPOSE

Artificial intelligence (AI) refers to technology capable of mimicking human cognitive functions and has important applications across all sectors and industries, including drug development. This has considerable implications for the regulation of drug development processes, as it is expected to transform both the way drugs are brought to market and the systems through which this process is controlled. There is currently insufficient evidence in published literature of the real-world applications of AI. Therefore, this narrative review investigated, collated, and elucidated the applications of AI in drug development and its regulatory processes.

METHODS

A narrative review was conducted to ascertain the role of AI in streamlining drug development and regulatory processes.

FINDINGS

The findings of this review revealed that machine learning or deep learning, natural language processing, and robotic process automation were favored applications of AI. Each of them had considerable implications on the operations they were intended to support. Overall, the AI tools facilitated access and provided manageability of information for decision-making across the drug development lifecycle. However, the findings also indicate that additional work is required by regulatory authorities to set out appropriate guidance on applications of the technology, which has critical implications for safety, regulatory process workflow and product development costs.

IMPLICATIONS

AI has adequately proven its utility in drug development, prompting further investigations into the translational value of its utility based on cost and time saved for the delivery of essential drugs.

Transverse Magnetic Surface Plasmons in Graphene Nanoribbon Qubits: The Influence of a VO2 Substrate

We study the influence of the phase-change material VO₂ on transverse magnetic (TM) surface plasmon (SP) modes in metallic arm-chair graphene nanoribbon (AGNR) qubits in the Lindhard approximation. We assess the effects of temperature as a dynamic knob for the transition from the insulating to the metallic phase on the TM SP modes in single-band (SB) and two-band (TB) transitions. We show that a VO₂ substrate leads to TM SP modes in both SB and TB transitions. In addition, we observe that the SP modes have a lower frequency than those for a substrate of constant permittivity. In addition, we study the influence of the substrate-induced band gap Δ' on SP modes in TB transitions for the insulating and metallic phases of VO₂.

Exploring the Current Practices, Costs and Benefits of FAIR Implementation in Pharmaceutical Research and Development: A Qualitative Interview Study

The findable, accessible, interoperable, reusable (FAIR) principles for scientific data management and stewardship aim to facilitate data reuse at scale by both humans and machines. Research and development (R&D) in the pharmaceutical industry is becoming increasingly data driven, but managing its data assets according to FAIR principles remains costly and challenging. To date, little scientific evidence exists about how FAIR is currently implemented in practice, what its associated costs and benefits are, and how decisions are made about the retrospective FAIRification of data sets in pharmaceutical R&D. This paper reports the results of semi-structured interviews with 14 pharmaceutical professionals who participate in various stages of drug R&D in seven pharmaceutical businesses. Inductive thematic analysis identified three primary themes of the benefits and costs of FAIRification, and the elements that influence the decision-making process for FAIRifying legacy data sets. Participants collectively acknowledged the potential contribution of FAIRification to data reusability in diverse research domains and the subsequent potential for cost-savings. Implementation costs, however, were still considered a barrier by participants, with the need for considerable expenditure in terms of resources, and cultural change. How decisions were made about FAIRification was influenced by legal and ethical considerations, management commitment, and data prioritisation. The findings have significant implications for those in the pharmaceutical R&D industry who are engaged in driving FAIR implementation, and for external parties who seek to better understand existing practices and challenges.

Artificial intelligence in drug development: present status and future prospects

Artificial intelligence (AI) uses personified knowledge and learns from the solutions it produces to address not only specific but also complex problems. Remarkable improvements in computational power coupled with advancements in AI technology could be utilised to revolutionise the drug development process. At present, the pharmaceutical industry is facing challenges in sustaining their drug development programmes because of increased R&D costs and reduced efficiency. In this review, we discuss the major causes of attrition rates in new drug approvals, the possible ways that AI can improve the efficiency of the drug development process and collaboration of pharmaceutical industry giants with AI-powered drug discovery firms.

Addressing the challenge of high-priced prescription drugs in the era of precision medicine: A systematic review of drug life cycles, therapeutic drug markets and regulatory frameworks

CONTEXT

Recent public outcry has highlighted the rising cost of prescription drugs worldwide, which in several disease areas outpaces other health care expenditures and results in a suboptimal global availability of essential medicines.

METHOD

A systematic review of Pubmed, the Financial Times, the New York Times, the Wall Street Journal and the Guardian was performed to identify articles related to the pricing of medicines.

FINDINGS

Changes in drug life cycles have dramatically affected patent medicine markets, which have long been considered a self-evident and self-sustainable source of income for highly profitable drug companies. Market failure in combination with high merger and acquisition activity in the sector have allowed price increases for even off-patent drugs. With market interventions and the introduction of QALY measures in health care, governments have tried to influence drug prices, but often encounter unintended consequences. Patent reform legislation, reference pricing, outcome-based pricing and incentivizing physicians and pharmacists to prescribe low-cost drugs are among the most promising short-term policy options. Due to the lack of systematic research on the effectiveness of policy measures, an increasing number of ad hoc decisions have been made with counterproductive effects on the availability of essential drugs. Future challenges demand new policies, for which recommendations are offered.

CONCLUSION

A fertile ground for high-priced drugs has been created by changes in drug life-cycle dynamics, the unintended effects of patent legislation, government policy measures and orphan drug programs. There is an urgent need for regulatory reform to curtail prices and safeguard equitable access to innovative medicines.

Big data investments in knowledge and non-knowledge intensive firms: what the market tells us

Purpose

This paper aims to investigate the value of big data investments by examining the market reaction to company announcements of big data investments and tests the effect for firms that are either knowledge intensive or not.

Design/methodology/approach

This study is based on an event study using data from two stock markets in China.

Findings

The stock market sees an overall index increase in stock prices when announcements of big data investments are revealed by grouping all the listed firms included in the sample. Increased stock prices are also the case for non-knowledge intensive firms. However, the stock market does not seem to react to big data investment announcements by testing the knowledge intensive firms along.

Research limitations/implications

This study contributes to the literature on assessing the economic value of big data investments from the perspective of big data information value chain by taking an unexpected change in stock price as the measure of the financial performance of the investment and by comparing market reactions between knowledge intensive firms and non-knowledge intensive firms. Findings of this study can be used to refine practitioners’ understanding of the economic value of big data investments to different firms and provide guidance to their future investments in knowledge management to maximize the benefits along the big data information value chain. However, findings of study should be interpreted carefully when applying them to companies that are not publicly traded on the stock market or listed on other financial markets.

Originality/value

Based on the concept of big data information value chain, this study advances research on the economic value of big data investments. Taking the perspective of stock market investors, this study investigates how the stock market reacts to big data investments by comparing the reactions to knowledge-intensive firms and non-knowledge-intensive firms. The results may be particularly interesting to those publicly traded companies that have not previously invested in knowledge management systems. The findings imply that stock investors tend to believe that big data investment could possibly increase the future returns for non-knowledge-intensive firms.

A real options approach to biotechnology investment policy-the case of developing a Campylobacter vaccine to poultry

The aim of the article is to identify and analyse public-private incentives for the development and marketing of new animal vaccines within a real options methodological framework, and to investigate how real options methodology can be utilized to support economic incentives for vaccine development in a cost-effective way. The development of a vaccine against Campylobacter jejuni in poultry is applied as a case study. Employing the real options methodology, the net present value of the vaccine R&D project becomes larger than a purely probabilistic expected present value throughout the different stages of the project - and the net present value becomes larger, when more types of real options are taken into consideration. The insight from the real options analysis reveals opportunities for new policies to promote the development of animal vaccines. One such approach might be to develop schemes combining stage-by-stage optimized subsidies in the individual development stages, with proper account taken of investors'/developers' economic incentives to proceed, sell or cancel the project in the respective stages. Another way of using the real options approach to support the development of desirable animal vaccines could be to issue put options for the vaccine candidate, enabling vaccine developers to hedge against the economic risk from market volatility.