In Search of Transcendence
The invariable mark of wisdom is to see the miraculous in the common - Ralph Waldo Emerson
Thousands of galaxies flood this near-infrared image of galaxy cluster SMACS 0723. High-resolution imaging from NASA’s James Webb Space Telescope combined with a natural effect known as gravitational lensing made this finely detailed image possible.
Introduction
I am a transcendence junkie. Fortunately, the feeling of transcendence comes easily to a grandfather. All I need to do is appreciate the enthusiasm in an 8-year-old as he discovers magnets and the curiosity of 4-year-olds as they turn over rocks to find bugs.
There is a notion that the feeling of transcendence mostly comes from spiritual experiences. Still, transcendence can also be evoked by the everyday moments, artistic experiences, and intellectual achievements that transport us beyond our day-to-day experiences.
When I see a deep space view from the James Webb Telescope, I'm in awe of the image and the technology that made it possible.
The James Webb Space Telescope, the most powerful and complex space observatory ever built, has captured stunning images of galaxies, nebulae, star clusters, and exoplanets, revealing new details and insights about the formation and evolution of the universe.
Consider the collaboration of individuals who combined their imagination, genius, perseverance, and luck to envision, design, and create technologies such as the Webb Telescope, the Large Hadron Collider, and, more recently, large language models like GPT-4o. These manifestations of human creativity and engineering skill remind us of what we can achieve when deeply connecting with others.
Of course, there are other sources of transcendence in science beyond technology. Sometimes, it can come from envisioning a new approach to acquiring and using knowledge.
Epistemology as a Source of Transcendence
Yes. Epistemology. The study of how we acquire knowledge and understand the world.
I first learned about "epistemology" in an essay by Lewis Sheiner entitled "The Intellectual Health of Clinical Drug Evaluation." Lewis wrote about knowledge acquisition in pharmaceutical research and development programs' pursuit of new drugs. He pointed out the inefficiency of using large-scale randomized clinical trials to learn about the clinical performance of new medicines.
He proposed an alternative approach: a modeling and simulation-based epistemology to synthesize existing knowledge and predict the likely outcomes of proposed clinical trials. With information about expected outcomes, we can determine whether to commit the resources to large-scale clinical trials that either confirm or refute the predictions.
These ideas led to the development of the 'Learn and Confirm' strategy, which has profoundly influenced pharmaceutical research and development, transforming the work of scientists and regulators. Modeling and simulations offer valuable insights into physiology and pharmacology, altering how we plan and conduct clinical trials, interpret outcomes, and forecast changes in the effectiveness and safety of medications for patients with varying characteristics.
Globalization of Pharmaceutical Research
Pharmaceuticals are a global commodity, and no matter how large or diverse the subjects in clinical trials are, numerous contexts in which drugs are used following market authorization will not have been studied.
Modeling and simulations are commonly used to forecast how drugs perform in various unexplored conditions. They can help determine the safety of specific drug combinations, whether certain patient characteristics necessitate different dosing recommendations, or whether patients should avoid the drug altogether. While particular studies may be necessary in some cases, modeling and simulations can often offer valuable guidance without empirical evidence.
A paper by Annette Gross and her colleagues highlights the importance of forward-looking risk assessments as new medicines are introduced worldwide.
While enhanced trial diversity is a critical step towards ensuring that results will be representative of patient populations, a concerted effort is required to characterize further the factors influencing interindividual and regional differences in response to global populations. Quantitative clinical pharmacology principles should be applied to allow extrapolation of data across groups or regions and provide insight into the effect of patient-specific characteristics on a medicine's dose rationale and efficacy and safety profiles.
Yet, despite this attention, the introduction of new drugs into naive populations is fraught with hazards. Regulators from low—and middle-income countries often have little or no information on the safety and efficacy of new medicines because relevant subjects are not included in the clinical trials, thus raising the specter of unintended safety consequences after market authorization.
For example, the introduction of efavirenz into HIV clinics in Uganda was accompanied by a high prevalence of severe CNS toxicity. Recently, the use of dolutegravir, an antiretroviral medication for treating HIV, has been linked to a high occurrence of hyperglycemia.
These and other examples emphasize the need for a new approach to introducing new medicines worldwide.
The Transcendence Potential of Fit-for-Purpose Modeling
Sometimes, the complexity of biology and pharmacology requires narrowing the scope of a model. These "fit-for-purpose" models have two requirements: the assumptions of model conditions must be correct and aligned with the context of interest, and the results from the narrowed scope should be accurate and useful.
Scientists recognize the limitations of these fit-for-purpose models and justify their approach with the observations of the statistician George Box:
"All models are wrong, some are useful."
A cynical scientist might question the "transcendence of epistemology" and say that modeling is just part of the day-to-day work of scientists and regulators. That may be where the current state of fit-for-purpose modeling has led us. It is just another drug-drug interaction to be modeled, and then move on to the next risk factor.
However, this approach to fit-for-purpose modeling overlooks a significant opportunity based on how we approach building these models and, more importantly, learning from and building upon them.
We are in the midst of a convergence of technology, science, process, and collaboration that offers tremendous possibilities for a new era of knowledge acquisition and the capability to leverage its potential for decision-making. In this case, the decision to approve market authorization for a drug in a specific country.
For example, regulatory agencies have invested tremendous time and energy in harmonizing requirements for submitting new drug applications to facilitate their review. At the same time, there has been explicit recognition of the potential for differences in the populations of different countries and the importance of systematically addressing these so-called ethnic factors.
Further, agencies are working together more often across different countries. As regulatory science and scientific research improve in low—and middle-income countries, they will gain opportunities to share insights and concerns currently unaddressed.
Finally, increased funding of global health research has provided new insights into the diverse contexts in LMIC, including the documentation of genetic diversity.
What if, amid this convergence, we systematically question the assumptions behind a fit-for-purpose model? What if we were to systematically check the degree to which the model is appropriate for different contexts? What if we created a space to entertain multiple perspectives on the assumptions and expand the definition of "fitness." What if we leverage new insights from unexplored places, incorporate them into future versions of the models, and broaden the applicability of models in different contexts across national boundaries? What if we could anticipate safety events that are only appreciated after safety issues arise in naive populations?
A way forward
Well-established platforms exist for modeling drugs' pharmacokinetic and pharmacodynamic properties. These platforms are already contributing to product labeling that reflects the needs of specific populations.
Regulatory agencies in low—and middle-income countries (LMICs) are well-positioned to use these platforms and help improve the global distribution of medications. Their unique experiences can provide new insights by identifying information gaps in advance and reducing uncertainty and associated risks.
An essay by Annamaria Carusi and her colleagues vividly describes their experiences in adapting existing frameworks depicting the molecular, cellular, or physiological events caused by a chemical. This adaptation provided a framework for an interdisciplinary effort to understand the biological pathways by which SARS-CoV-2 causes disease.
The scientific and social rewards of the collaboration were well expressed in an interview with one of the participants:
They went on to talk about the moment each realized there were specific parallels and differences between a virus and a chemical as stressors, an understanding that would not have been obvious without the framework. The excitement of the discovery was palpable; it was clearly a joyful moment that satisfied scientific curiosity and spoke to their core reasons for being scientists. This was compounded by the collaborative nature of this discovery, in a kind of scientific unity with another person—the very opposite of an individualistic competition.
Let's contemplate the amazing opportunities of augmenting global human networks of scientists and regulators with artificial intelligence to address complex challenges, like the worldwide introduction of new medications. By acknowledging that networks of regulators and scientists are guided by rules, modeling frameworks, and a commitment to delivering new medicines to patients, we can discover fresh approaches to collaborate and guarantee the safety, effectiveness, and global distribution of medicines.
Jonathan Sacks describes this source of transcendence in his book, The Great Partnership: Science, Religion, and the Search for Meaning,
There may be a new synthesis in the making. It will be very unlike the Greek thought-world of the medieval scholastics with its emphasis on changelessness and harmony. Instead, it will speak about the emergence of order, the distribution of intelligence and information processing, the nature of self-organizing complexity, the way individuals display a collective intelligence that is a property of groups, not just the individuals that comprise them, the dynamic of evolving systems and what leads some to equilibrium, others to chaos. Out of this will emerge new metaphors of nature and humanity, flourishing and completeness.
Sometimes, simple acts such as a hug, a smile, or a meaningful conversation can lead to a profound experience.
I propose seeking a sense of transcendence in the more complex task of advancing the social and scientific foundations for new approaches to the challenge of the worldwide distribution of new medicines.
I would appreciate your thoughts on the progress in improving the global distribution of drugs, ideas for enhancing the process, and the challenges that would be encountered in bringing the ideas in this essay to fruition.