Clinic Exploratory research Preclinical

3D printing and artificial intelligence: the future of galenics?

“Ten years from now, no patient will take the same thing as another million people. And no doctor will prescribe the same thing to two patients.”

Fred Paretti from the 3D drug printing startup Multiply Labs.

3D printing – also known as additive manufacturing – is one of the technologies capable of transforming pharmaceutical development, and will certainly play a role in the digitalization of the drug manufacturing sector. This short article will attempt to provide an overview of how 3D printing works, its various use cases in the manufacture of personalized medicines, the current regulatory framework for this innovative technology, and the synergies that may exist with Artificial Intelligence.

3D printing, where do we stand?

The principle of 3D printing, developed since the early 2000s and now used in a large number of industrial fields, consists of superimposing layers of material in accordance with coordinates distributed along three axes (in three dimensions) following a digital file. This 3D file is cut into horizontal slices and sent to the 3D printer, allowing it to print one slice after another. The terminology “3D printing” brings together techniques that are very different from each other:

  • The deposition of molten wire or extrusion: a plastic wire is heated until it melts and deposited at points of interest, in successive layers, which are bound together by the plastic solidifying as it cools. This is the most common technique used by consumer printers.
  • The photopolymerization of the resin: a photosensitive resin is solidified with the help of a laser or a very concentrated light source, layer by layer. This is one of the techniques that allows a very high level of detail.
  • Sintering or powder fusion: a laser is used to agglomerate the powder particles with the energy it releases. This technique is used to produce metal or ceramic objects.

In the pharmaceutical industry, 3D printing is used in several ways, the main ones being :

  • The realization of medical devices, using the classic techniques of printing plastic or metallic compounds or more particular techniques allowing medical devices to acquire original properties, like the prostheses of the start-up Lattice Medical allowing adipose tissue to regenerate.
  • Bio-printing, allowing, by printing with human cells, to reconstitute organs such as skin or heart patches, like what is done by another French start-up: Poietis
  • Finally, and this is what will be discussed in this article, 3D printing also has a role to play in galenics by making it possible to print, from a mixture of excipient(s) and active substance(s), an orally administered drug.

What are the uses of 3D printing of medicines? 

3D printing brings an essential feature to drug manufacturing: flexibility. This flexibility is important for:

  • Manufacturing small clinical batches: clinical phases I and II often require small batches of experimental drugs for which 3D printing is useful: it is sometimes economically risky to make large investments in drug manufacturing at this stage. Moreover, it is often necessary to modify the active ingredient content of the drugs used, and 3D printing would enable these batches to be adapted in real time. Finally, 3D printing can also be useful for offering patients placebos that are as similar as possible to their usual treatments.
  • Advancing towards personalized medicine: 3D printing of drugs allows the creation of “à la carte” drugs by mixing several active ingredients with different contents for each patient. In the case of patients whose weight and absorption capacities vary over time (children or the elderly who are malnourished, for example), 3D printing could also adapt their treatments in real time according to changes in their weight, particularly in terms of dosage and speed of dissolution.

To address these issues, most major pharmaceutical companies are increasingly interested in 3D printing of drugs. They are investing massively in this field or setting up partnerships, like Merck, which is cooperating with the company AMCM in order to set up a printing system that complies with good manufacturing practices. The implementation of this solution has the potential to disrupt the traditional manufacturing scheme, as illustrated in the diagram below.

Figure 1 – Modification of the manufacturing steps of a tablet by implementing 3D printing (Source : Merck)


The first commercialized 3D printed drug was approved by the FDA in 2015. Its active ingredient is levetiracetam. The goal of using 3D printing for this drug was to achieve a more porous tablet that dissolves more easily and is more suitable for patients with swallowing disorders. Despite these initial approvals and market accesses, the regulatory environment has yet to be built, as it is still necessary to assess the changes in best practices that 3D printing technology may impose and determine what types of tests and controls should be implemented. Destructive quality controls are not particularly well suited to the small batches produced by the 3D printer technique. To our knowledge, there are currently no GMP-approved 3D printers for the manufacture of drugs.

Will the future of drug 3D printing involve artificial intelligence? 

A growing number of authors believe that 3D printing of drugs will only be able to move out of the laboratory and become a mainstream technology in industry if artificial intelligence is integrated. Indeed, as things stand at present, because of the great flexibility mentioned above, the use of 3D printing requires a long iterative phase: it is necessary to test thousands of factors concerning in particular the excipients used, but also the parameters of the printer and the printing technique to be selected. The choice of these different factors is currently made by the galenics team according to its objectives and constraints: what is the best combination of factors to meet a given pharmacokinetic criterion? Which ones allow to minimize the production costs? Which ones allow to respect a possible regulatory framework? Which ones allow for rapid production? This iterative phase is extremely time-consuming and capital-intensive, which contributes to making 3D printing of drugs incompatible with the imperatives of pharmaceutical development for the moment. Artificial Intelligence seems to be the easiest way to overcome this challenge and to make the multidimensional choice of parameters to be implemented according to the objectives “evidence-based”. Artificial Intelligence could also be involved in the quality control of the batches thus manufactured.

The use of Artificial Intelligence to design new drugs opens up the prospect of new technical challenges, particularly with regard to the availability of the data required for these Machine Learning models, which are often kept secret by pharmaceutical laboratories.  We can imagine that databases can be built by text-mining scientific articles and patents dealing with different galenic forms and different types of excipients and then completed experimentally, which will require a significant amount of time. In addition to these technical challenges, it will also be necessary to ask more ethical questions, particularly with regard to the disruption of responsibilities caused by the implementation of these new technologies: who would be responsible in the event of a non-compliant batch being released? The manufacturer of the 3D printer? The developer of the algorithm that designed the drug? The developer of the algorithm that validated the quality control? Or the pharmacist in charge of the laboratory?

All in all, we can conclude that 3D printing of medicines is a technology that is already well mastered, whose market is growing by 7% each year to reach a projected market of 440 million dollars in 2025, but whose usefulness is so far limited to certain cases of use, but which could tomorrow, due to the unlocking of its potential through the combination of Artificial Intelligence, allow us to achieve a fully automated and optimized galenic development and manufacturing of oral forms, finally adapted to the ultra-customized medicine that is coming.

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To go further:

  • Moe Elbadawi, Laura E. McCoubrey, Francesca K.H. Gavins, Jun J. Ong, Alvaro Goyanes, Simon Gaisford, and Abdul W. Basit ; Disrupting 3D Printing of medicines with machine learning ; Trends in Pharmacological Sciences, September 2021, Vol 42, No.9
  • Moe Elbadawi, Brais Muñiz Castro, Francesca K H Gavins, Jun Jie Ong, Simon Gaisford, Gilberto Pérez , Abdul W Basit , Pedro Cabalar , Alvaro Goyanes ; M3DISEEN: A novel machine learning approach for predicting the 3D printability of medicines ; Int J Pharm. 2020 Nov 30;590:119837
  • Brais Muñiz Castro, Moe Elbadawi, Jun Jie Ong, Thomas Pollard, Zhe Song, Simon Gaisford, Gilberto Pérez, Abdul W Basit, Pedro Cabalar, Alvaro Goyanes ; Machine learning predicts 3D printing performance of over 900 drug delivery systems ; J Control Release. 2021 Sep 10;337:530-545. doi: 10.1016/j.jconrel.2021.07.046
  • Les médicaments imprimés en 3D sont-ils l’avenir de la médecine personnalisée ? ; 3D Natives, le média de l’impression 3D ;!
  • Les médicaments de demain seront-ils imprimés en 3D ? ; Le mag’ Lab santé Sanofi ;
  • Press Releases – Merck and AMCM / EOS Cooperate in 3D Printing of Tablets ;

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Why are we still conducting meta-analyses by hand?

« It is necessary, while formulating the problems of which in our further advance we are to find solutions, to call into council the views of those of our predecessors who have declared an opinion on the subject, in order that we may profit by whatever is sound in their suggestions and avoid their errors. »

Aristotle, De anima, Book 1, Chapter 2

Systematic literature reviews and meta-analyses are essential tools for synthesizing existing knowledge and generating new scientific knowledge. Their use in the pharmaceutical industry is varied and will continue to diversify. However, they are particularly limited by the lack of scalability of their current methodologies, which are extremely time-consuming and prohibitively expensive. At a time when scientific articles are available in digital format and when Natural Language Processing algorithms make it possible to automate the reading of texts, should we not invent meta-analyses 2.0? Are meta-analyses boosted by artificial intelligence, faster and cheaper, allowing more data to be exploited, in a more qualitative way and for different purposes, an achievable goal in the short term or an unrealistic dream?

Meta-analysis: methods and presentation

A meta-analysis is basically a statistical analysis that combines the results of many studies. Meta-analysis, when done properly, is the gold standard for generating scientific and clinical evidence, as the aggregation of samples and information provides significant statistical power. However, the way in which the meta-analysis is carried out can profoundly affect the results obtained.

Conducting a meta-analysis therefore follows a very precise methodology consisting of different stages:

  • Firstly, a search protocol will be established in order to determine the question to be answered by the study and the inclusion and exclusion criteria for the articles to be selected. It is also at this stage of the project that the search algorithm is determined and tested.
  • In a second step, the search is carried out using the search algorithm on article databases. The results are exported.
  • Articles are selected on the basis of titles and abstracts. The reasons for exclusion of an article are mentioned and will be recorded in the final report of the meta-analysis.
  • The validity of the selected studies is then assessed on the basis of the characteristics of the subjects, the diagnosis, and the treatment.
  • The various biases are controlled for in order to avoid selection bias, data extraction bias, conflict of interest bias and funding source bias.
  • A homogeneity test will be performed to ensure that the variable being evaluated is the same for each study. It will also be necessary to check that the data collection characteristics of the clinical studies are similar.
  • A statistical analysis as well as a sensitivity analysis are conducted.
  • Finally, the results are presented from a quantitative and/or non-quantitative perspective in a meta-analysis report or publication. The conclusions are discussed.

The systematic literature review (SLR), unlike the meta-analysis, with which it shares a certain number of methodological steps, does not have a quantitative dimension but aims solely to organize and describe a field of knowledge precisely.

The scalability problem of a powerful tool

The scalability problem is simple to put into equation and will only get worse over time: the increase in the volume of data generated by clinical trials to be processed in literature reviews is exponential while the methods used for extracting and processing these data have evolved little and remain essentially manual. The intellectual limits of humans are what they are, and humans cannot disrupt themselves.

As mentioned in the introduction to this article, meta-analyses are relatively costly in terms of human time. It is estimated that a minimum of 1000 hours of highly qualified human labor are required for a simple literature review and that 67 weeks are needed between the start of the work and its publication. Thus, meta-analyses are tools with a high degree of inertia and their temporality is not currently adapted to certain uses, such as strategic decision-making, which sometimes requires certain data to be available quickly. Publications illustrate the completion of full literature reviews in 2 weeks and 60 working hours using automation tools using artificial intelligence.

“Time is money”, they say. Academics have calculated that, on average, each meta-analysis costs about $141,000. The team also determined that the 10 largest pharmaceutical companies each spend about $19 million per year on meta-analyses. While this may not seem like a lot of money compared to the various other expenses of generating clinical evidence, it is not insignificant and it is conceivable that a lower cost could allow more meta-analyses to be conducted, which would in turn explore the possibility of conducting meta-analyses of pre-clinical data and potentially reduce the failure rate of clinical trials – currently 90% of compounds entering clinical trials fail to demonstrate sufficient efficacy and safety to reach the market.

Reducing the problem of scalability in the methodology of literature reviews and meta-analyses would make it easier to work with data from pre-clinical trials. These data present a certain number of specificities that make their use in systematic literature reviews and meta-analyses more complex: the volumes of data are extremely large and evolve particularly rapidly, the designs of pre-clinical studies as well as the form of reports and articles are very variable and make the analyses and the evaluation of the quality of the studies particularly complex. However, systematic literature reviews and other meta-analyses of pre-clinical data have different uses: they can identify gaps in knowledge and guide future research, inform the choice of a study design, a model, an endpoint or the relevance or not of starting a clinical trial. Different methodologies for exploiting preclinical data have been developed by academic groups and each of them relies heavily on automation techniques involving text-mining and artificial intelligence in general.

Another recurring problem with meta-analyses is that they are conducted at a point in time and can become obsolete very quickly after publication, when new data have been published and new clinical trials completed. So much time and energy is spent, in some cases after only a few months or weeks, to present inaccurate or partially false conclusions. We can imagine that the automated performance of meta-analyses would allow their results to be updated in real time.

Finally, we can think that the automation of meta-analyses would contribute to a more uniform assessment of the quality of the clinical studies included in the analyses. Indeed, many publications show that the quality of the selected studies, as well as the biases that may affect them, are rarely evaluated and that when they are, it is done according to various scores that take few parameters into account – for example, the Jadad Score only takes into account 3 methodological characteristics – and this is quite normal: the collection of information, even when it is not numerous, requires additional data extraction and processing efforts.

Given these scalability problems, what are the existing or possible solutions?

Many tools already developed

The automation of the various stages of meta-analyses is a field of research for many academic groups and some tools have been developed. Without taking any offence to these tools, some examples of which are given below, it is questionable why they are not currently used more widely. Is the market not maturing enough? Are the tools, which are very fragmented in their value proposition, not suitable for carrying out a complete meta-analysis? Do these tools, developed by research laboratories, have sufficient marketing? Do they have sufficiently user-friendly interfaces?

As mentioned above, most of the tools and prototypes developed focus on a specific task in the meta-analysis methodology. Examples include Abstrackr, which specialises in article screening, ExaCT, which focuses on data extraction, and RobotReviewer, which is designed to automatically assess bias in reports of randomised controlled trials.

Conclusion: improvement through automation?

When we take into account the burgeoning field of academic exploration concerning automated meta-analysis as well as the various entrepreneurial initiatives in this field (we can mention in particular the very young start-up:, we can only acquire the strong conviction that more and more, meta-analysis will become a task dedicated to robots and that the role of humans will be limited to defining the research protocol, assisted by software that will allow us to make the best possible choices in terms of scope and search algorithms. Thus, apart from the direct savings that will be made by automating meta-analyses, many indirect savings will be considered, particularly those that will be made possible by the best decisions that will be taken, such as whether or not to start a clinical trial. All in all, the automation of meta-analyses will contribute to more efficient and faster drug invention.

Resolving Pharma, whose project is to link reflection and action, will invest in the coming months in the concrete development of meta-analysis automation solutions.

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To go further:
  • Marshall, I.J., Wallace, B.C. Toward systematic review automation: a practical guide to using machine learning tools in research synthesis. Syst Rev 8, 163 (2019).
  • Clark J, Glasziou P, Del Mar C, Bannach-Brown A, Stehlik P, Scott AM. A full systematic review was completed in 2 weeks using automation tools: a case study. J Clin Epidemiol. 2020 May;121:81-90. doi: 10.1016/j.jclinepi.2020.01.008. Epub 2020 Jan 28. PMID: 32004673.
  • Beller, E., Clark, J., Tsafnat, G. et al. Making progress with the automation of systematic reviews: principles of the International Collaboration for the Automation of Systematic Reviews (ICASR). Syst Rev 7, 77 (2018).
  • Lise Gauthier, L’élaboration d’une méta-analyse : un processus complexe ! ; Pharmactuel, Vol.35 NO5. (2002) ;
  • Nadia Soliman, Andrew S.C. Rice, Jan Vollert ; A practical guide to preclinical systematic review and meta-analysis; Pain September 2020, volume 161, Number 9,
  • Matthew Michelson, Katja Reuter, The significant cost of systematic reviews and meta-analyses: A call for greater involvement of machine learning to assess the promise of clinical trials, Contemporary Clinical Trials Communications, Volume 16, 2019, 100443, ISSN 2451-8654,
  • Vance W. Berger, Sunny Y. Alperson, A general framework for the evaluation of clinical trial quality; Rev Recent Clin Trials. 2009 May ; 4(2): 79–88.
  • A start-up specializing in meta-analysis enhanced by Artificial Intelligence:
  • And finally, the absolute bible of meta-analysis: The handbook of research synthesis and meta-analysis, Harris Cooper, Larry V. Hedges et Jefferey C. Valentine

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