Challenges and opportunities in the development of complex generic long-acting injectable drug products

O’Brien, Matthew N.; Jiang, Wenlei; Wang, Yan; Loffredo, David M.

doi:10.1016/j.jconrel.2021.06.017

Cited by 55 publications

(40 citation statements)

References 48 publications

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“…The unparalleled chemical and physical diversity afforded by polymers, makes polymer-based LAIs a particularly apt version of this drug delivery strategy. These systems can be engineered to entrap drug within a polymer matrix with release occurring via erosion, diffusion, or simultaneous erosion and diffusion mechanisms [3]. In addition to achieving sustained or controlled drug release, the encapsulation of drug into these polymeric matrices can often provide protection to the therapeutic cargo [4].…”

Section: Main Textmentioning

confidence: 99%

“…Despite the advantages associated with polymeric LAIs, their translation from bench to bedside remains non-trivial. In the past two decades, only about 30 polymeric LAI products have received regulatory approval, and this is in contrast to the thousands of conventional oral formulations approved in the same period [3,8]. Several pivotal challenges limit the clinical translation of polymeric LAIs.…”

Section: Main Textmentioning

confidence: 99%

“…Firstly, there are few biodegradable polymer materials that are generally recognized as safe (GRAS) for parenteral administration. To date, the polymeric LAIs which have received clinical approval are largely based on a single polymer: poly(lactide-co-glycolide) (PLGA) [2,3,9]. The use of a material, such as PLGA, with an established safety profile may accelerate the regulatory approval of new LAI formulations.…”

Section: Main Textmentioning

confidence: 99%

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Machine Learning Predictions of Drug Release from Polymeric Long Acting Injectables

Bannigan

Häse

Aldeghi

et al. 2021

Preprint

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Machine learning is enabling leap-step advances in a number of fields including drug discovery and materials science. The current study explores the application of machine learning to address a critical challenge in pharmaceutical formulation development: the prediction of drug release profiles from polymer-based long-acting injectables. Long acting injectables are considered one of the most promising therapeutic strategies for the treatment of chronic diseases as they can afford improved therapeutic efficacy, safety, and patient compliance. The use of polymer materials in such a drug formulation strategy can offer unparalleled diversity owing to the ability to synthesize materials with a wide range of properties. However, the interplay between multiple parameters, including the physicochemical properties of the drug and polymer, make it near to impossible to predict the performance of these systems a priori. This results in a need to develop and characterize a wide array of formulation candidates through extensive and time-consuming in vitro experimentation. In this study, various neural network architectures are constructed and trained, resulting in accurate predictions of drug release profiles that agree with experimental data. The simplicity with which these broadly applicable machine learning models are identified, using a limited amount of training data, is evidence of the promising potential of data-driven approaches in advanced pharmaceutical formulation development.

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Section: Main Textmentioning

confidence: 99%

Section: Main Textmentioning

confidence: 99%

Section: Main Textmentioning

confidence: 99%

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Machine Learning Predictions of Drug Release from Polymeric Long Acting Injectables

Bannigan

Häse

Aldeghi

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…PLGA-based LAI drug products allow extended release over long periods of time and require low dosing frequency [ 2 ], thereby increasing patient compliance [ 3 , 4 , 5 ]. To date, 25 PLGA-based long-acting drug products, all in the injectable or implantable forms, have been approved by the FDA ( Table 1 ) [ 6 ]. However, these PLGA-based LAI drug products are relatively expensive for most patients [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Challenges and Complications of Poly(lactic-co-glycolic acid)-Based Long-Acting Drug Product Development

Lim

Tan

et al. 2022

Pharmaceutics

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Poly(lactic-co-glycolic acid) (PLGA) is one of the preferred polymeric inactive ingredients for long-acting parenteral drug products that are constituted of complex formulations. Despite over 30 years of use, there are still many challenges faced by researchers in formulation-related aspects pertaining to drug loading and release. Until now, PLGA-based complex generic drug products have not been successfully developed. The complexity in developing these generic drug products is not just due to their complex formulation, but also to the manufacturing process of the listed reference drugs that involve PLGA. The composition and product attributes of commercial PLGA formulations vary with the drugs and their intended applications. The lack of standard compendial methods for in vitro release studies hinders generic pharmaceutical companies in their efforts to develop PLGA-based complex generic drug products. In this review, we discuss the challenges faced in developing PLGA-based long-acting injectable/implantable (LAI) drug products; hurdles that are associated with drug loading and release that are dictated by the physicochemical properties of PLGA and product manufacturing processes. Approaches to overcome these challenges and hurdles are highlighted specifically with respect to drug encapsulation and release.

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“…These systems can be engineered to entrap drug within a polymer matrix with release occurring via various mechanisms including erosion, diffusion, simultaneous erosion and diffusion, etc. 3 In addition to achieving sustained or controlled drug release, the encapsulation of drug into these polymeric matrices can often protect therapeutic cargo. 4 To date, various polymer-based LAI technologies administered via the intramuscular, 5 subcutaneous, 6 and intra-articular 7 routes have received regulatory approval (Scheme 1A).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Models to Accelerate the Design of Polymeric Long-Acting Injectables

Bannigan

Bao

Hickman

et al. 2022

Preprint

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Long-acting injectables are considered one of the most promising therapeutic strategies for the treatment of chronic diseases as they can afford improved therapeutic efficacy, safety, and patient compliance. The use of polymer materials in such a drug formulation strategy can offer unparalleled diversity owing to the ability to synthesize materials with a wide range of properties. However, the interplay between multiple parameters, including the physicochemical properties of the drug and polymer, make it very difficult to intuitively predict the performance of these systems. This necessitates the development and characterization of a wide array of formulation candidates through extensive and time-consuming in vitro experimentation. Machine learning is enabling leap-step advances in a number of fields including drug discovery and materials science. The current study takes a critical step towards data driven drug formulation development with an emphasis on long-acting injectables. A series of machine learning algorithms were trained and refined for accurate prediction of experimental drug release profiles. Analysis of the best performing model uncovered the properties of the drug and polymer that were identified to be key determinants of drug release. This information can be used to identify promising drug-polymer combinations that result in long-acting injectables with specific drug release behaviour. Importantly, implementation of this data driven approach has the potential to reduce the time, cost and resources associated with formulation development. Datasets and relevant codes used to train the machine learning models have been made openly available to encourage implementation in future drug formulation efforts.

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Challenges and opportunities in the development of complex generic long-acting injectable drug products

Cited by 55 publications

References 48 publications

Machine Learning Predictions of Drug Release from Polymeric Long Acting Injectables

Machine Learning Predictions of Drug Release from Polymeric Long Acting Injectables

Challenges and Complications of Poly(lactic-co-glycolic acid)-Based Long-Acting Drug Product Development

Machine Learning Models to Accelerate the Design of Polymeric Long-Acting Injectables

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