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342 Z. Rahman et al.
associated with DMF (Drug Master File). The sponsor can request a Type C
meeting under the heading “Type C meeting – request to participate in the Emerging
Technology Program” for IND, NDA, and BLA or “Pre-ANDA meeting – request
to participate in the Emerging Technology Program.” Request should be submitted
at least 3 months before the planned submission of the applications. The request
should contain the following information (FDA
2017a, b):
1. Brief description of the proposed technology
2. A brief explanation why the proposed technology is substantially innovative and
unique and should be considered under this program
3. How the proposed technology could potentially improve product safety, identity,
strength, quality, or purity
4. Summary of development plan and perceived roadblocks to implementation
(technical or regulatory)
5. Timeline of submission
The FDA approved the first 3D printed drug product Spritam ® under this
program (Jain and Rana
2018). Recently, a Chinese pharmaceutical company has
also received IND clearance for its 3D printed drug product “T19” under the ETT
program (Everett
2021).
Irrespective of their uniqueness, 3D printed drug products do not need new
regulatory pathway. Current regulatory pathways of NDA, ANDA, and BLA can
be used to file an application for review. An NDA for a 3D printed drug can be
submitted via the 505(c) Section of the CFR under 505(b)(1) or 505(b)(2) regulatory
pathway. Submission under 505(b)(1) pathway requires extensive nonclinical and
clinical data to prove proposed drug product safety and efficacy for the indication
being sought (CFR21
n.d.). 505(b)(1) regulatory pathway is typically used for a
drug that has never been approved by the FDA. NDA approved under this pathway
is typically awarded 5-year marketing exclusivity, 7 years for orphan drugs with
6-month pediatric exclusivity, or 5-year exclusivity extension under “Generating
Antibiotic Incentives Now” (FDA
2018). 505(b)(2) regulatory pathway is another
route that can be used for NDA application. This is typically followed for drugs that
were previously approved via 505(b)(1) regulatory pathway. In this pathway, the
sponsor relies on the FDA’s finding of safety and/or effectiveness of a listed drug(s)
(approved drug product) under Section 314.54 or published literature. The listed
drug product(s) are the products approved under 505(b)(1) pathway, but not under
505(j). The FDA usually requires sponsors to perform a bridge study to support the
safety and/or efficacy of the submission. These studies may include bioavailability,
bioequivalence, clinical efficacy, or nonclinical studies. The applicant may or may
not have a right of reference to raw data. The sponsor may file NDA under
505(b)(1) if the sponsor obtains the right of reference to the raw data. Applications
covered under 505(b)(2) are new dosage forms, new strength, new route, change in
active ingredient (salt, ester, complex, chelate, clathrate, racemate, or enantiomer),
combination products, etc. If a listed drug is protected by exclusivity, filing or
approval of the 505(b)(2) application may be delayed. Unlike 505(b)(1), submission
10 Regulatory Perspective of Additive Manufacturing in the Field . . . 343
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Fig. 10.3 Regulatory pathway for 3D printed drug product approvals
under 505(b)(2) requires patent certification claiming drug or method of use. For
filing of NDA, the FDA requires 12 months’ data generated at 25
◦
and 6 months’ data at 40
is typically awarded 3-year marketing exclusivity (FDA
for orphan and pediatric exclusivities. Spritam
relies on the safety and efficacy of Keppra (NDA207958
C/75% RH (FDA 2003). NDA approved via 505(b)(2)
®
was approved under 505(b)(2) that
1999) and is also eligible
n.d.).
◦
C/60% RH
ANDA (generics) of reference listed drugs (RLDs) is approved under Sections
505(b)(1) and 505(b)(2) (Fig.
under Hatch-Waxman Amendments 1984 and 505(j) of the Federal Food, Drug,
and Cosmetic Act (FD&C Act). Generics of Spritam
10.3). Regulatory pathway for generic was established
®
can be approved after the
expiration of marketing exclusivity period. It was approved in 2015, and 3-year
marketing exclusivity expired in 2018. For generic product approval, similarity in
manufacturing methods between ANDA and RLD is not required by the agency.
The sponsor has to demonstrate pharmaceutical equivalence and bioequivalence
between ANDA and RLD. For example, the sponsor of ANDA of Spritam
have to manufacture its generic version by 3DP process. Furthermore, the sponsor
can choose a completely different 3DP process. Spritam
®
is manufactured by BJ
®
does not
process. The sponsor can manufacture by using any of the 3DP processes or even
traditional pharmaceutical manufacturing methods. However, the sponsor has to
demonstrate the effect of process on the quality, in vitro performance, and stability.
For approval of ANDA, the FDA requires 6 months of data generated at 25
RH and 40
◦
C/75% RH (FDA 2014). Generally, in-use stability is not required
◦
C/60%
for this type of dosage form; however, the agency decides it on a case-by-case
basis. Furthermore, in-use stability testing condition is not defined by the FDA or
industry guidance documents. However, the paper published from the FDA lab uses.
Comparative bioequivalence study should demonstrate that the rate and extent of test
product are similar to RLD. Rate and extent are demonstrated by T
max
and C
max
,
344 Z. Rahman et al.
Fig. 10.4 Various modules of common technical documents
and AUC, respectively. The FDA specifies 80–125% test/reference ratio criteria for
C
,AUCt, and AUC
max
at 90% confidence interval. Generics of 3D printed drug
inf
products may be eligible for bioequivalence study biowaiver, provided that the drugs
belong to BCS Class I and III drugs (FDA
2021). Spritam ® contains levetiracetam
that is a BCS I drug. Thus, it may be eligible for biowaiver. Certain ANDA may
be eligible for 180-day exclusivity under the Amendment. Patent certification is
required for ANDA submission, just as it is for a 505(b)(2) application.
NDA, BLA, and ANDA of 3D printed drug products are submitted in electronic Common Technical Document (eCTD) format. eCTD is organized into five
modules, namely 1, 2, 3, 4, and 5 (Fig.
10.4). Module 1 contains administrative
information of the sponsors and regulatory information such as patent information.
Module 2 contains a summary of quality, clinical and nonclinical studies. Module
3 embodies quality information of drug substance, drug products, chemistry,
manufacturing control, stability, analytical methods, etc. Modules 4 and 5 contain
details of nonclinical and clinical study reports, respectively (ICH
2016).
10 Regulatory Perspective of Additive Manufacturing in the Field . . . 345
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Unlike drug products, the FDA has issued guidance on technical considerations
specific to devices manufactured using additive manufacturing (FDA
2017a, b). In
comparison to drug products, the FDA has approved many devices manufactured
by 3DP. FDA classifies medical devices into three classes I, II, and III. Regulatory
control increases from Class I to Class III. The majority of Class I devices and some
Class II devices are exempt from Premarket Notification 510(k). Most of the Class
II devices also require Premarket Notification 510(k), whereas Class III devices
require Premarket Approval (PMA). Devices are submitted to the Center for Devices
and Radiological Health (CDRH) for review and approval. Combination products
can be manufactured by 3DP processes. A combination product can combine drugs,
devices, and/or biological products for therapeutic and diagnostic purposes. The
Office of Combination Products (OCP) develops regulations and guidance for
combination products. The OCP issues classification and jurisdiction assignments
for human medical products. The classification of a product determines the type of
a product (drug, device, biological product, or combination product). Jurisdiction
determines the FDA Center or Lead Center (CBER, CDER, or CDRH) that will
regulate the product.
3DP has wide acceptability and potential for dispensing of personalized medicine
in a hospital or pharmacy setting. In 2021, approximately 4.69 billion prescriptions
were filled, with compounded drugs accounting for 1–3% of prescriptions in the
United States (Statista 2022; Jackson et al. 2020). Healthcare professionals and the
FDA frequently raise concerns regarding the quality, safety, and efficacy of compounded drug products (Gudeman et al.
2013). These concerns may be eliminated
or reduced by the use of 3DP in filling compounded prescriptions that will ensure
consistency in quality. Implementation of 3DP in compounding does not need an
oversight of the FDA. However, they still have to meet regulatory requirements of
the pharmacy board of the state where they are operating. Compounding in a retail
pharmacy and hospital setting is exempt from the Food, Drug, and Cosmetic Act
(FD&C) (145), Sections 501(a)(2)(B) (concerning cGMP requirements), 502(f)(1)
(concerning the labeling of drugs with adequate directions for use), and 505
(concerning the approval of drugs under new drug applications or abbreviated new
drug applications). However, to be exempted from 501(a)(2)(B), 502(f)(1), and 505
of the FD&C Act, it must meet the criteria listed in the relevant Acts (Rahman et al.
2018, Charoo et al. 2020).
10.7 Summary
3D printing is considered an advanced manufacturing method that has wide
application in pharmaceutical and biomedical fields. Currently, Spritam
only 3DP drug product commercially available. However, many pharmaceutical
companies are working to develop specialized products and combination products.
Manufacturing methods do not determine the regulatory pathway, and regardless
of the manufacturing method, the requirements for quality, safety, and efficacy
®
is the
346 Z. Rahman et al.
remain the same. Current regulatory pathways for IND, NDA, BLA, and ANDA
can be utilized for filing of products manufactured using 3D printing processes.
Implementation of 3D manufacturing of devices and drug products in pharmacy,
hospital, or point of care does not need the FDA oversight.
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Machine Learning in Additive Manufacturing
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of Pharmaceuticals
Tatsuaki Tagami, Koki Ogawa, and Tetsuya Ozeki
Abstract
The application of machine learning and deep learning in additive manufacturing,
also called 3D printing, is expected in industrial fields to be an effective method
to optimize the manufacturing process, to control the quality of 3D printed
objects, to detect defects in the objects, and to predict material properties. In
the pharmaceutical field, 3D printed medicine has been approved by the United
States Food and Drug Administration, and since then, 3D printing technology has
been attracting attention, even creating a new model of tailored medicine. The
3D printing of pharmaceutical products needs a trial-and-error process due to the
complex printing parameters as well as the physical properties of the printer ink,
which is the drug formulation in this case. Machine learning may hold promise
in solving the complex problems of drug manufacturing using 3D printers. This
review introduces recent articles about 3D printed medicine and the application
of machine learning. We also include recent articles about 3D printed medicine
that use statistical approaches in the experimental methods. Finally, we discuss a
possible future where “artificial intelligence pharmacists” will regularly use 3D
printers in a clinical setting.
11
Keywords
3D printed medicine · Additive manufacturing · Machine learning · Design of
experiment · Artificial intelligence (AI) pharmacist
T. Tagami () · K. Ogawa · T. Ozeki
Drug Delivery and Nano Pharmaceutics, Graduate School of Pharmaceutical Sciences,
Nagoya City University, Nagoya, Aichi, Japan
e-mail:
tagami@phar.nagoya-cu.ac.jp
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023
S. Banerjee (ed.), Additive Manufacturing in Pharmaceuticals,
https://doi.org/10.1007/978-981-99-2404-2_11
349
350 T. Tagami et al.
11.1 Introduction
3D printing technology is being developed and has already been put to practical use
in the industrial fields. Various related research and development are also gaining
traction in the pharmaceutical field. In the pharmaceutical industry, 3D printed
tablets that are easily dissolved in water using ZipDose technology (SPRITAM
Aprecia Pharmaceuticals: URL:
https://www.aprecia.com/) were approved in the
United States in 2015. Many studies have been conducted to determine the
usefulness of 3D printed tablets and pharmaceuticals, and the number of papers
in English about 3D printed tablets has increased rapidly in the past 5 years.
Since 3D printers can produce drugs of various sizes, shapes, and internal structures on demand, it is expected to be applied to creating custom-made formulations
tailored to the individual patient, and various examples of this application have
been reported (Trenfield et al.
2019;Jamrózetal. 2018; Melocchi et al. 2020).
While pharmaceutical factories generally use a single building to mass-produce
pharmaceuticals, 3D printers are thought to make it possible for pharmacists to
prepare drugs in hospitals and other medical institutions because they do not require
much space. One application of 3D printing technology, polypills that combine
multiple active pharmaceutical ingredients (APIs) into one tablet, has also been
devised (Khaled et al.
2015; Pereira et al. 2019; Alayoubi et al. 2022). This is
expected to improve medication adherence when a patient who has lifestyle-related
diseases has to take medicine every day. In addition, 3D printing is expected to be
used in producing pediatric preparations. Some commercially available medicines
do not have pediatric applications, so pharmacists may need to grind down adult
tablets or capsules to prepare pediatric doses (Galande et al.
2020). These can be
difficult to swallow for pediatric patients, and the exposed bitterness may end up
causing the patient to refuse the medication. An effective method has been devised
using a 3D printer to prepare drug formulations (e.g., chewable tablets in sizes
(dose), shapes, colors, and flavors that are easy for children to swallow) (Goyanes et
al.
2019; Han et al. 2022; Aita et al. 2020). 3D printers are also suitable for small-
batch production used in clinical trials. In addition, for on-demand manufacturing
of drugs, 3D printing allows flexible dosing (Pietrzak et al. 2015;Arafatetal.
2018; Öblom et al. 2019). 3D printers have also been proposed in therapeutic drug
monitoring, wherein the drug concentration must be determined for the individual
patient due to a narrow therapeutic window. In addition, although it is still at the
conceptual stage, ideas have been suggested to enable the emergency 3D printing of
pharmaceuticals that have been suspended for some reason and also to enable the
on-demand preparation and use of drugs that are difficult to store (Norman et al.
2017). In this way, it is expected that the use of 3D printers will continue to expand
for various medical purposes.
For the typical materials used in 3D printers in the industry, smooth manufacturing can be performed by optimizing the experimental conditions. According to the
American Society for Testing and Materials International, there are seven types of
3D printers, and to the best of our knowledge, five types are used in pharmaceutical
®
,
11 Machine Learning in Additive Manufacturing of Pharmaceuticals 351
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preparation research: material extrusion (e.g., fused deposition modeling (FDM)
and pressure-assisted microsyringe (PAM)), binder jetting, vat photopolymerization
(e.g., stereolithography (SLA) and digital light processing (DLP)), material jetting
(inkjet), and powder bed fusion (e.g., selective laser sintering (SLS)). For example,
FDM, which is one of the most studied types of material extrusion, uses polylactic
acid (PLA)-based polymer filaments commonly in the industry (Ilyas et al.
2021),
and a variety of PLA filaments for 3D printing are commercially available. PLA is a
plant-based polymer and is well known as a biodegradable plastic. The combination
of 3D printers and biodegradable plastics is expected to be a model that is in line
with the Sustainable Development Goals and has low environmental burden, and
FDM 3D printers can be used to produce the required amount of target products on
demand based on optimized printer conditions to some extent. On the other hand,
pharmaceuticals have complex compositions with many unknown factors, as will be
described later, making it difficult to predict physical properties when 3D printing
them. An effective solution for better prediction of the many factors that can affect
the desired outcome of 3D printing may be the use of machine learning, including
deep learning.
Machine learning is mentioned by Arthur Samuel as the field of study that
gives computers the ability to learn without being explicitly programmed (Mahesh
2020). Machine learning can improve and optimize the quality and process of many
applications, resulting in cost reduction in various industry examples including in
3D printing (Meng et al. 2020). This technique has also been applied for drug
discovery and development (Vamathevan et al.
and materials science (Butler et al.
2018). Machine learning can be divided into
2019) as well as in molecular
several types of algorithms such as regression, classification, and clustering (Fig.
11.1). Two types of machine learning are supervised and unsupervised learning, as
some algorithms require existing data labels, while others do not. Deep learning
Fig. 11.1 Concept of machine learning in the development of 3D printed medicine
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