Requirements, expectations, challenges and opportunities associated with training the next generation of pharmacometricians

The Center for Pharmacometrics and Systems Pharmacology (CPSP) at the University of Florida has been engaging stakeholders from industry and regulatory agencies in recent years to seek input on the requirements and expectations for next generation pharmacometricians in the workplace. The objective of this article is to share our joint perspective on identified key skills with the broader pharmacometrics community in order to initiate a collective consensus building process on how to best develop them. Pharmacometrics has evolved from a descriptive science to an applied science that is increasingly used in all phases of drug development over the last decades. Today's application for accelerating and streamlining drug development is referred to as model-informed drug development (MIDD) or, more broadly, model-informed drug discovery and development (MID3).1, 2 Due to the increasing application of MID3 approaches, rapid emergence of new data analysis and computational methods as well as increasing complexity of drug development and regulatory evaluation processes, demands for and toward pharmacometricians have been evolving over the years as well.3-5 It is no longer sufficient to master a certain tool or technical skill. Instead, these skills need to be applied in a team-based environment to solve a drug development problem. Strong technical skills and the ability to identify when pharmacometrics analyses can be used to answer a particular question are of course the foundation for every pharmacometrician. In addition to these foundational technical skills, it is our firm belief that a successful pharmacometrician should ideally: (1) be an effective communicator, (2) be able to think strategically, and (3) be able to influence team-based decisions, as outlined in Figure 1. A solid foundation of scientific knowledge (e.g., basic pharmacokinetic/pharmacodynamic [PK/PD] and pharmacology concepts) and technical pharmacometrics skills is the basis for being able to successfully apply MID3 approaches.6 Foundational technical skills include but are not limited to nonlinear mixed effects (NLME) PK/PD modeling, mechanistic PK/PD modeling, including physiologically-based pharmacokinetic (PBPK), quantitative systems pharmacology (QSP) modeling and clinical trial simulations, as shown in Table 1. We believe that a strong technical skill set comprises familiarity with pharmacometric methods, software, and programming language(s), the ability to critically assess the scientific validity of a model and its associated parameter values, as well as knowledge in pathophysiology, PKs, pharmacology, toxicology, statistics, and mathematics. These foundational technical skills are essential to influence decision-making in drug discovery and development as well as during regulatory review. With the increased use of MID3 approaches in drug discovery and early drug development,1 familiarity with emerging sciences and technologies, such as machine learning, artificial intelligence, and models relating structural properties of chemical compounds to potency/safety/PK, are becoming more important. As a consequence, the spectrum of required technical pharmacometrics skill sets has widened even further and two questions arise. To which extent do pharmacometricians need to cover the entire width of the spectrum? Is there a need for specialization among pharmacometricians? Strategic thinking entails the ability to anticipate both challenges and opportunities and to plan a course of action accordingly.8 Strategic thinking in the context of drug development requires a thorough understanding of the drug development process, applicable regulations and guidelines, and appreciation of organizational constraints (cost, time, value/risk). Obtaining this understanding requires time and is often the reason why junior pharmacometricians have difficulty leveraging MID3 approaches to streamline and accelerate drug development. Exposing junior pharmacometricians to drug development problems already during their training program (e.g., during internships in industry/regulatory agencies or joint research projects with industry/regulatory agencies) is consequently important. On the other hand, MID3 approaches provide an opportunity to facilitate strategic thinking because they allow for the integration of complex knowledge from multiple sources, explore different scenarios by quantifying assumptions, and prospectively choose the one that best meets the organization's goal (e.g., most pragmatic or has the highest probability of success). The combination of technical and strategic skills ensures that pharmacometricians can identify critical questions in drug development programs that may be answered using MID3 approaches. Strategic skills as well as the ability to influence and negotiate are essential for identifying critical questions within and across project teams, provide teams with an option for decision making based on modeling and simulation, and conveying the solutions in a manner that engages the audience. The ability to systematically integrate information and extrapolate beyond what has already been studied holds great potential for influencing decisions in drug development and regulatory approval. To broaden the impact that pharmacometricians can have on a final decision, it is important for her/him to be involved in all phases of drug discovery and development, including prospective study design, execution of the study, analyzing the data, and performing simulations for the next trial(s) along with making go/no-go decisions. Establishing this mindset early on in focused teaching and research curricula that integrate drug discovery and development with pharmacometrics is consequently beneficial. At the same time, it is important to remember that decision making is a complex process, which is affected by evidence, beliefs, assumptions and bias, a combination recurrent in common judgments. Biases in judgments reveal heuristics in our thinking under uncertainty, which can lead to severe and systematic errors. Decision making is also impacted by the way a scenario is framed. For example, a 90% chance of success would likely be perceived more favorably than a 10% risk of failure, although they are mathematically the same.9 We believe that pharmacometricians must understand this interplay and continuously educate themselves on how to maximize the impact of MID3 with the overall goal in mind (i.e., to accelerate and streamline drug development and ultimately improve patient care). At the same time, they must be willing to make trade-offs, when necessary (i.e., be adaptive and pragmatic to achieve consensus amongst team members). On top of the general increase in demand for pharmacometricians, there is an imbalance among supply, demand, and professional working opportunities between different regions of the world, resulting in geographic and academic brain drain.10 Particularly the latter poses an imminent threat to the pharmacometrics community because if this trend continues, we will soon reach a point where we will no longer have a sufficient number of academicians, particularly at the Associate and Full Professor level, that are able to train next generation pharmacometricians. To overcome these challenges, a general rethinking of traditional, siloed “business models” toward joint efforts between academia, industry, and regulatory agencies will be required. These efforts can be established at various levels, ranging from loose affiliations, such as adjunct appointments or internship opportunities for students and trainees, to structured partnerships with a dedicated logistic, financial, educational, and research infrastructure support. The latter would allow to overcome limitations of individual stakeholders (e.g., limited time for developing concepts or platform models outside the direct drug development pipeline or teaching drug development without having worked in the industry) and provide planning security (e.g., proactive workforce pipeline development, PhD and postdoctoral support for the duration of the training program, or increased utilization of large-scale databases for disease platform model development) for all parties involved. These joint efforts would also allow for the development of applied training modules, where stakeholders bring their individual strengths to the table (i.e., concepts and hands-on software training [academia], drug development context and possibly data [industry], regulatory context [regulators]). Combining forces would also allow us to stay abreast with the rapidly evolving drug development and regulatory evaluation landscape and offer training for new modalities, concepts, and analysis approaches in a timely fashion. Ideally, these partnerships would be interdisciplinary in nature to enable a broader vision to problems and ultimately spark innovation by crossing traditional knowledge boundaries. A transdisciplinary approach that integrates, for example, PBPK, machine learning, and artificial intelligence or pharmacometrics and pharmacoepidemiology, would also further a mindset of constant learning and collaboration, which is key to success in team-based environments. To facilitate the interactions, we collectively composed a list of proposed teaching and training activities needed for developing technical, strategic, as well as communication and influencing skills (Table 1). We recognize that this list, although too lengthy for any single PhD or postdoctoral fellowship program, is not all-encompassing and that the outlined activities should be tailored toward the individual trainee's educational background and working experience. We also recognize that training activities in academia may have to be complemented by downstream activities. For example, two-way sabbaticals may allow working professionals from industry or regulatory agencies to retool in academia, whereas academicians could stay abreast with latest advances in drug discovery, development, and regulatory evaluation while spending time in the industry or at the agency. Finally, we do not intend to infringe on individual faculty's freedom to train their students as they see fit, dismiss previous curricula proposal,6 or suggest that academia should take sole responsibility for the proposed teaching and training activities. We rather intend to use this proposal to spark a broader conversation among stakeholders in the pharmacometrics arena to collectively build consensus on key skills and outline viable avenues for how to best develop them. As such, we invite all stakeholders to join this conversation and welcome any constructive feedback on our proposal. The authors would like to thank Benjamin Weber for his input into the manuscript. No funding was received for this work. The authors declared no competing interests for this work.