Early Strategizing Tightens Development Timelines

BioPharm International, BioPharm International, June 2022 Issue, Volume 35, Issue 6
Pages: 10–15

Having a clear clinical strategy early on can shave time off overall development projects.

Speed-to-market is a primary motivating factor in biopharmaceutical development, and delays or disruptions have costly consequences. The need for moving from research/preclinical stages to clinical stages of development requires a clinical development strategy, which, when coupled with innovative approaches in R&D, can accelerate overall project timelines.

Consequences from delays

Any delays in the development of a new biotherapeutic can have major consequences for both the sponsor, any co-sponsors and contract service providers with whom a sponsor may partner. Parties can incur increased costs of development, delayed time to reaching an inflection point, regulatory filing, approval or launch, which in turn equates to lost commercial revenue, delayed upfront and/or milestone payments from partnering deals. Ultimately, this can also result in a loss of credibility with external parties, investors/the markets, which can impact the success of subsequent fundraising, says Nick Meyers, PhD, vice-president Product Development, Boyds, a UK-headquartered drug development consultancy.

Joe Sinclair, vice-president, Business Development & Corporate Strategy, Vibalogics, adds that, for private and public organizations, delays in development can have negative financial consequences outside of the need for remediation and can translate into investors losing confidence in supporting ongoing activities with the same prior optimism. “In worst-case scenarios, delays during active clinical enrollment or ongoing treatment regimens can all-too-often result in a patient missing the window of opportunity to receive a prior planned treatment. It is, therefore, critical that these delays or disruptions are avoided with robust clinical development strategies in place,” he states.

Inadequate planning is another issue that may affect speed-to-market, says Jennifer Thayer, vice-president, Medical, Regulatory, and Scientific Communications, and principal consultant, BioBridges, a US-based clinical development services provider. Thayer notes that “while developing a strategy and associated tactics are important, documenting a feasible and executable clinical development plan (CDP)—and maintaining it—can be key to overall success.”

Frequently, a CDP is thrown together to address immediate needs, such as obtaining funding or regulatory agency feedback, Thayer points out, thus losing the benefits of the process by focusing only on the immediate deliverable. In fact, “the process of developing and maintaining CDPs can be extremely advantageous for internal consensus-building, providing a shared location for tracking and managing key data, piquing interest and capabilities from cross-functional team members, and serving as a point-of-focus for recalibrating as needed,” Thayer notes.

Maintaining a truly useful CDP requires discipline. “Because a change in one deliverable can have both upstream and downstream consequences on the development and commercialization of a new drug or device, the CDP should be revisited—in its entirety—at least quarterly during management reviews,” says Thayer. “Of course, the benefit of a quality CDP is not a new idea for those in drug/device development … yet we find that CDPs are not regularly maintained nor, therefore, well utilized. Why do these issues persist?”

Thayer surmises that the most likely cause of inadequate CDP maintenance is an unlikely suspect: the documentation tool. Most CDPs “live” in a PowerPoint presentation, which, although a great tool for static communication, is inadequate for maintaining dynamic (i.e., constantly changing) content. “Using the wrong tool for the job may result in a botched outcome. Ultimately, leaders need to demand and invest in state-of-the art, dynamic communication and knowledge management tools to wholly achieve cutting-edge clinical research,” Thayer emphasizes.

Meanwhile, from the patient perspective, when therapies fail in Phase II and Phase III clinical trials for lack of efficacy, everybody loses, says David Gray, chief scientific officer, Inscopix, a US-based brain-mapping platform company. Failures in later-stage clinical development means patients and their families have to wait longer for relief; and, unfortunately, companies often have to close their doors or abandon disease areas in which they have been heavily invested after these failures. “These failures contribute to higher drug costs and lower perception of the industry among the general public,” Gray says.

Tightening up discovery

Starting from the very beginning, having a good approach to R&D and/or drug discovery workflows can kickstart an efficient clinical development strategy for new biomolecules. New drug development is a costly and time-consuming process, emphasizes Mark Behlke, MD, PhD, chief scientific officer, Integrated DNA Technologies (IDT), a global genomics solutions provider that specializes in the development, manufacture, and marketing of nucleic acid products. “It is estimated that >90% of drug candidates fail clinical trials.”

Gray, meanwhile, notes that where one ends up in clinical development is fundamentally tied to the direction in which one begins the journey—more specifically, the quality of the therapeutic hypothesis, the translational relevance of the data being used, and the ability to bring the right insights into the optimization process. Gray cautions that having a good compound and a good clinical development strategy and process cannot make up for and overcome the error of working on the wrong target.

“Some of the most promising and impactful new technologies are designed to speed up and improve the process of selecting new therapeutic approaches with high chance of showing efficacy in the clinic,” Gray says.

Arguably, some of the biggest gains in terms of maximizing time efficiencies in pharma/biotech development programs can come from the ability to parallel track activities “at risk”, which requires a willingness to invest more funding upfront, in return for a greater return on investment, says Meyers. However, having funding available to invest more upfront is simply not possible for many start-ups and companies at the seed funding stage, Meyers adds.

“Purely sequential development is now rarely acceptable, as ground to competitors and patent life are ceded, which means that there will be an increased front-loading in terms of cost, which seasoned investors in the sector are used to and understand,” Meyers states. “This, in turn, means that robust tools and software are used to build a detailed project plan/roadmap with clear stage gates and go-no-go decision points, and the ability to track key milestones.”

Meyers explains that with a skilled project manager providing or coordinating the appropriate development and regulatory support, data can be mapped to and summarized in the relevant electronic common technical document eCTD, investigational new drug (IND) modules and investigational medicinal product dossier (IMPD) shells on an ongoing basis, which permits the identification of “gaps” in the data package at an early stage, thereby reducing the time to prepare regulatory submissions/dossiers.

The strategic attack

Having a clinical development strategy in place is important for establishing contingencies in the event that things do not go as planned. These contingencies could help mitigate delays as well as costs if the proper precautions have been made for difficulties that occur during the development process, according to James J. Hickman, PhD, chief scientist, Hesperos, a US-based company specializing in human-on-a-chip technology, and professor at the University of Central Florida. There should furthermore be a wide spectrum of possible contingencies available to correct any delays in process efficiently with a known estimate of cost beforehand, he emphasizes.

Moreover, having a well-developed strategy in place allows for better clarity and alignment between the drug developer, collaborating service providers, clinical sites, regulators, and investors, adds Sinclair. Such a strategy will also ensure that aspirations and reality are connected in an executable plan.

“The success of drug development relies on the comprehensive alignment, preparation, and contribution of all in the supporting network. The clinical development strategy must be established from the earliest point possible with broad considerations in mind while being prepared to make adjustments along the way,” Sinclair states. He further explains that, when the need for alterations in the strategy arises, it is important to also consider the downstream secondary and tertiary impacts, and to ensure that those impacted receive suitable communications to strengthen realignment.

It is also critical to be deliberate and strategic about maximizing the chance of success in clinical trials, adds Gray, who points out the importance of strategizing early, beginning in early research when ideas are being evaluated for resourcing. “Especially in neuroscience, the traditional vetting of efficacy potential has a poor track record. Companies that can use emerging technologies and approaches strategically in their vetting and drug discovery processes to advance programs with higher chances of demonstrating clinical efficacy will have a tremendous advantage in terms of lower average cost per successful program and shorter average time to successful new product,” Gray states.

Optimizing the speed and efficiency of a development program requires putting in place a target product profile (TPP) as early as possible; a willingness to consult widely and make use of new technologies and tools to assist and accelerate decision-making; and, most importantly, integrated project planning coupled with scenario and risk-mitigation planning, all of which require experienced industry program managers, says Meyers. “Industry program managers should also be working iteratively with their scientific and commercial colleagues and should constantly interrogate and re-interrogate plans to secure consistent funding needed to support activities carried out in parallel and/or at risk,” he states.

Technology lends a hand

Emerging technologies that accelerate drug development include functional genomics and machine learning. Advances in functional genomics tools and the use of machine learning methods can reduce failure rates and accelerate the progression of new drugs to market, says Behlke. Using one example, Behlke explains that, in 2011, AstraZeneca revised its concept of the drug development pathway, implementing what it called the “5R Framework”, which focused on the five biggest “whys” that led to drug failure (1). The first issue addressed was to ensure that the right target was being worked on (the first “R”). According to Behlke, target identification and validation has long been a focus of drug development R&D, yet many drugs continue to fail in clinical trials because of a lack of efficacy. Much of this failure can be attributed to inadequate target validation. “Any potential target must be fully vetted to be certain that it is causal or relevant to the disease and that modulating it will improve outcome,” Behlke states.

Functional genomics applies genetic, molecular, and cellular methods to understand gene function; this is the cornerstone of target validation, Behlke points out. Historically, target validation involved random mutagenesis to study the effects of gene disruption and complementary DNA expression systems to model gene activation. The advent of RNA interference (RNAi) provided the ability to systematically study the effects that reducing expression had for every gene. This RNAi approach was limited, however, by off-target effects (OTEs) that primarily stemmed from unwanted participation of the RNAi reagents in the cell’s natural microRNA gene-regulatory network (2), Behlke notes.

Efforts to streamline and accelerate drug development, especially during clinical development, continue apace across the industry, Meyers observes. For example, the use of artificial intelligence (AI) is now employed routinely to model clinical trial outcomes and the impact of different designs/randomization schema, enabling the optimization of clinical trials and minimizing sample size. “Other innovations include the use of biometric technologies for trial subject verification; enhanced bedside technology (especially non-invasive imaging technology and the use of micro amounts of whole blood) to monitor multiple biomarkers simultaneously, which reduces the need for traditional sample collection and analysis; and fully automated clinical trial data and laboratory management systems (e.g., clinical trial management system, laboratory information management systems) that facilitate data monitoring and cleaning, and reduce the time to database lock,” says Meyers. In other parts of the drug development continuum, there have also been advances (e.g., in manufacturing) that employ the use of computer simulation that can predict optimal formulations and processing conditions and minimize the number of steps during scale-up.

In fact, machine learning and AI are transforming the entire drug development pathway (3), Behlke emphasizes. Often, adoption of AI-based methods requires specialized personnel to implement, modify, or build software tools within an organization; thus, drug development organizations have invested heavily in hiring bioinformaticians/computational scientists (4) who have strong backgrounds combining math, computer science, and biology/chemistry.

“Early and effective collaboration between wet-lab and computational scientists allows for optimized design of experiments so that data output directly links into analysis pipelines, statistical tests, and predictive models. The greatest benefit to the drug development workflow sometimes requires building new tools to fully leverage the potential of artificial intelligence or machine learning methods (5),” says Behlke. He further explains that interdisciplinary communication is fostered by developing a common language (i.e., key terms/concepts) and following collaboration guidelines (6). With a collaborative culture in place, computational scientists can transform and accelerate drug development workflows, which can lower costs and reduce the time to market.

Among the approaches being used to accelerate drug development is the organ-on-a-chip or multi-organ human-on-a-chip approaches, which have already shown the ability to provide unique insights into therapeutic development as well as provide avenues for therapeutic development that did not previously exist, such as for rare diseases, says Hickman.

Hickman points to a recent study in which the ability to generate efficacy data for an IND was demonstrated using human-on-a-chip methodology (7). Generating the efficacy data for an IND allowed for a drug with Phase II safety data to be repurposed for a new indication, and this led to the authorization of a clinical trial (NCT04658472) for a rare disease. Another benefit from these multi-organ-on-a-chip human systems lays in the fact that these systems can measure efficacy and off-target toxicity in the same recirculating system, which enables the determination of a therapeutic index at the evaluation stage for a lead compound (8). “This would allow a medicinal chemist to evaluate many variations of the lead compound early in the development before having to make a decision on which variation to scale up to produce much larger quantities of a potential therapeutic needed for animal studies,” says Hickman.

The organ-on-a-chip approach could save time and reduce costs because using this approach would mean a reduction in the number of animals used for preclinical studies. In addition, the organ-on-a-chip approach can increase the chances of success for a chosen therapeutic candidate. “This is also beneficial for later stages of development as companion diagnostics can be developed concurrently with therapeutic development,” Hickman notes.

Furthermore, the human organ-on-a-chip method would be beneficial if contradictory results emerge between different animal models, because a human model of the disease would already be available. Even after entering clinical trials there exists the possibility to quickly and efficiently address any issues that may arise, says Hickman.

Gray, meanwhile, points out that, for years, neuroscience research has relied heavily on behavioral assessments in preclinical species to evaluate the potential efficacy of new therapies; and this reliance on behavior has often resulted in poor outcomes. Recently, however, technologies are emerging that allow highly detailed and direct measurement of brain activity in preclinical species.

“These approaches transcend well-recognized limitations of behavioral assessments and directly address sources of poor translation to clinical efficacy,” Gray states. For instance, there is now a technology (Inscopix brain-mapping technology) that allows for real-time recording of hundreds to thousands of neurons while also monitoring behavior at a detailed level, and to which AI can then be applied to extract neurocircuitry patterns.

“Analysis of these multi-modal datasets is a new and powerful way of assessing the efficacy and impact of a novel therapeutic intervention on the brain circuitry itself. Building discovery programs and clinical development strategies on these new brain-based efficacy assessments (that are expected to have higher predictive power for selecting the right targets) directly addresses the biggest source of cost and delay (in aggregate) in drug discovery,” Gray explains.

Moving from research/preclinical stages to clinical stages of development requires significant points of hand-off from one team and group to another with a different set of skills and priorities. Having all parties aligned on a development strategy, documented on a transparent executable CDP, helps ensure the chances of success.


1. P. Morgan, et al., Nat Rev Drug Discov 17, 167–181 (2018).
2. X. Lin, et al., Nucleic Acids Res. 33 (14) 4527–4535 (2005).
3. S. Dara, et al., Artif Intell Rev. 55 (3) 1947–1999 (2022).
4. Transparency Market Research, “Bioinformatics Market to Expand Due to Increase in R&D Activities by Drug Manufacturers, States TMR Report,” Press Release, Feb. 23, 2022.
5. E. Ferrero, et al., PLoS Computational Biology 8, 1–10 (2020).
6. F. Sahneh, et al., PLoS Computational Biology 17 (5) e1008879 (2021).
7. J.W. Rumsey, et al., Adv. Therap. 2200030 (2022).
8. C.W. McAleer, et al., Sci Trans Med 11, eaav1386 (2019).

About the author

Feliza Mirasol is the science editor for BioPharm International.

Article Details

BioPharm International
Vol. 35, No. 6
June 2022
Pages: 10–15


When referring to this article, please cite it as F. Mirasol, “Early Strategizing Tightens Development Timelines,” BioPharm International 35 (6) 10–15 (2022).