Abstract
The complex nature of human diseases, the significant differences between individuals, their own specificity and the inevitable variability in how a treatment is administered make pharmacological research extremely difficult. Due to the inability to completely predict how a product will affect individual patients, it’s not unusual for a drug to show serious problems during clinical tests after having performed well in pre-clinical and laboratory tests. The strategy of in silico Clinical Trials (ISCT) comes as a possible bridge from the insights of molecular understanding to ad personam medical treatments. By refining and complementing traditional clinical trials, ISCTs should provide a proxy for biological conditions. The in silico strategy is typically justified by appealing to the power of big data, that computational strategies involve. Big data seem to imply a different methodology and thus a different way of doing research, thought to be more adequate to address biological complexity. In this chapter we analyze such a view by showing how these new computational strategies enhance and implement traditional approaches rather than changing the nature of clinical investigation. An epistemic look at ISCTs shows how such an issue is addressed in pharmacological studies. The moral of in silico approaches is that complexity cannot be fully addressed per se, only through heuristics. Contra enthusiastic claims arguing for a paradigm shift in scientific research, ISCTs do not change the theoretical account governing current scientific practice, nor the general framework according to which discovery strategies are adopted and justified. On the other hand, ISCTs do provide a change in the methodologies. However, such a novelty should be regarded as a complement and not as a replacement.
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Notes
- 1.
See for instance Bothwell et al. 2016 and Frieden 2017 at https://www.statnews.com/2017/08/02/randomized-controlled-trials-medical-research/
- 2.
“radiography in which a three-dimensional image of a body structure is constructed by computer from a series of plane cross-sectional images made along an axis — called also computed axial tomography, computerized axial tomography, computerized tomography” (definition retrieved from Merriam Webster Dictionary at https://www.merriam-webster.com/dictionary/computed%20tomography)
- 3.
- 4.
On the other side, the proponents of Avicenna Project suggest that their strategy can also unravel mechanistic features of the phenomena under investigation. See above.
- 5.
Interestingly, unlike many other controversies over the nature of scientific work, which have been led mainly by philosophers, this time scientists themselves are embracing such a discussion. The unusual interest of many researchers for epistemological problems might be also explained by the fact that, precisely because of the economic impact that such new approaches have brought to research itself and the promise they present on the nature of diagnosis and treatment, in silico strategies may actually change not just what science and clinical research are in theory (thus affecting pure theoretical speculation) but also what they are in practice. Indeed, such a change could potentially reshape how scientific institutions, research centers, clinical hospitals and funding agencies (either public or private) are organized, structured and the way they act to foster their activities. Bruce Alberts for instance (2010), is worried about the possibility that so called “small-science” labs (those laboratories still not equipped with strong in silico technologies) might disappear thus creating a disparity among research projects and approaches in terms of funds and visibility. Indeed, if high impact-factor journals will privilege a certain kind of research, this will eventually lead to a sort of hierarchy of discoveries, according to which certain kinds of evidence (i.e. pure mechanistic description) might count less than global representations provided by computational approaches.
- 6.
Concerning this point, it is important to analyze what makes the difference here in terms of heuristic strategies and their justification. In their Discovering Complexity: Decomposition and Localization as Strategies in Scientific Research (1993), Bechtel and Richardson argue that the amount of information that is available to scientists when they start their investigation is, practically and theoretically, limited. This means that not all possible logical solutions could be pursued, nor even fully listed. Nevertheless, no research begins out of the blue. Scientists normally adopt a set of variable rules and methods (i.e. heuristics) which help them in settling and understanding the type of questions they want to ask and how they want to answer them. However, heuristics do not have the power of logical principle. They are prone to err and they lack the power of generality since they are usually highly context dependent and bound to specific experimental conditions. Contrary to naive views, this fact is far from being a flaw of research strategy. As Bechtel and Richardson argue, “[l]imiting the relevant variables and imposing assumptions […] are procedures for attacking the problem. They also describe partial solutions. Applied to scientific problem-solving, this would mean that the heuristic assumptions constitutive of explanatory models would be critical in a developing research program” (Bechtel and Richardson 1993, p 16). Heuristics then starts from the assumption that complexity cannot be fully addressed per se. ‘Decomposition’ here means that fundamental causal factors (mechanistically intended) and interactions are detected and considered more important than others. From a methodological point of view, decomposition can be structural, dissecting a system in physical parts, and functional, investigating the activity of a system in terms of its sub-activities. Localization then is the connection between a particular process and the entity that is responsible for that process. Only at a first glance computational approaches seem to proceed in a different way. The volume of data involved in such a perspective may let someone think that there is no need to decompose nor to localize systems, since the very system itself will be addressed as a whole. This is somehow true, but only in a specific sense. In fact, the global view provided by the capacity of computers in handling and displaying enormous amounts of data coming from different resources is not a way to overcome traditional discovery strategies but rather a way to broaden it and make it more efficient.
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Boem, F., Malagrinò, I., Bertolaso, M. (2020). In Silico Clinical Trials: A Possible Response to Complexity in Pharmacology. In: LaCaze, A., Osimani, B. (eds) Uncertainty in Pharmacology. Boston Studies in the Philosophy and History of Science, vol 338. Springer, Cham. https://doi.org/10.1007/978-3-030-29179-2_6
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