Nanomedicine Ex Machina: Between Model-Informed Development and Artificial Intelligence

Revisiting nanomedicine design strategies for follow-on products: A model-informed approach to optimize performance

The field of nanomedicine is undergoing a seismic transformations with the rise of nanosimilars, reshaping the pharmaceutical landscape and expanding beyond traditional innovators and generic manufacturers. Nanodrugs are increasingly replacing conventional therapies, offering improved efficacy and safety, while the demand for follow-on products drives market diversification. However, the transition from preclinical to clinical stages presents challenges due to the complex biopharmaceutical behavior of nanodrugs. This review highlights the integration of Quality-by-Design (QbD), in vitro-in vivo correlations (IVIVCs), machine learning, and Model-Informed Drug Development (MIDD) as key strategies to address these complexities. Additionally, it discusses the role of high-throughput processes in the optimization of the nanodrug development pipelines. Covering generations of delivery systems from liposomes to RNA-loaded nanoparticles, this review underscores the evolving market dynamics driven by recent advances in nanomedicine.

The Role of Artificial Intelligence and Machine Learning in Accelerating the Discovery and Development of Nanomedicine

The unique potential of nanomedicine to address challenging health issues is rapidly advancing the field, leading to the generation of more effective products. However, these complex systems often pose several challenges with respect to their design for specific functionality, scalable manufacturing, characterization, quality control, and clinical translation. In this regard, the application of artificial intelligence (AI) and machine learning (ML) approaches can enable faster and more accurate data assessment, identifying trends and predicting outcomes, leading to efficient nanomedicine product development. This perspective paper discusses the potential of AI and ML in nanomedicine product development with a focus on their applications in discovery, assessment, manufacturing, and clinical trials. The potential limitations of AI and ML approaches in nanomedicine product development are also covered.

A bio-predictive release assay for liposomal prednisolone phosphate

Predictive performance assays are crucial for the development and approval of nanomedicines and their bioequivalent successors. At present, there are no established compendial methods that provide a reliable standard for comparing and selecting these formulation prototypes, and our understanding of the in vivo release remains still incomplete. Consequently, extensive animal studies, with enhanced analytical resolution for both, released and encapsulated drug, are necessary to assess bioequivalence. This significantly raises the cost and duration of nanomedicine development. This work presents the development of a discriminatory and biopredictive release test method for liposomal prednisolone phosphate. Using model-informed deconvolution, we identified an in vivo target release. The experimental design employed a discrete L-optimal configuration to refine the analytical method and determine the impact of in vitro parameters on the dosage form. A three-point specification evaluated the key phases of in vivo release: early (T-5%), intermediate (T-20%), and late release behavior (T-40%), compared to the in vivo release profile of the reference product, NanoCort®. Various levels of shear responses and the influence of clinically relevant release media compositions were tested. This enabled an assessment of the effect of shear on the release, an essential aspect of their in vivo deformation and release behavior. The type and concentration of proteins in the medium influence liposome release. Fetal bovine serum strongly impacted the discriminatory performance at intermediate shear conditions. The method provided deep insights into the release response of liposomes and offers an interesting workflow for in vitro bioequivalence evaluation.

Tolerogenic Nano-/Microparticle Vaccines for Immunotherapy

Autoimmune diseases, allergies, transplant rejections, generation of antidrug antibodies, and chronic inflammatory diseases have impacted a large group of people across the globe. Conventional treatments and therapies often use systemic or broad immunosuppression with serious efficacy and safety issues. Tolerogenic vaccines represent a concept that has been extended from their traditional immune-modulating function to induction of antigen-specific tolerance through the generation of regulatory T cells. Without impairing immune homeostasis, tolerogenic vaccines dampen inflammation and induce tolerogenic regulation. However, achieving the desired potency of tolerogenic vaccines as preventive and therapeutic modalities calls for precise manipulation of the immune microenvironment and control over the tolerogenic responses against the autoantigens, allergens, and/or alloantigens. Engineered nano-/microparticles possess desirable design features that can bolster targeted immune regulation and enhance the induction of antigen-specific tolerance. Thus, particle-based tolerogenic vaccines hold great promise in clinical translation for future treatment of aforementioned immune disorders. In this review, we highlight the main strategies to employ particles as exciting tolerogenic vaccines, with a focus on the particles' role in facilitating the induction of antigen-specific tolerance. We describe the particle design features that facilitate their usage and discuss the challenges and opportunities for designing next-generation particle-based tolerogenic vaccines with robust efficacy to promote antigen-specific tolerance for immunotherapy.

Advancements in Nanoparticle Characterization

Nanotechnology for drug delivery has made significant advancements over the last two decades. Innovations have been made in cancer research and development, including chemotherapies, imaging agents, and vaccine strategies, as well as other therapeutic areas, e.g., the recent commercialization of mRNA lipid nanoparticles as vaccines against the SARS-CoV-2 virus. The field has also seen technological advancements to aid in addressing the complex questions posed by these novel therapies. In this latest edition of protocols and methods for nanoparticle characterization, we highlight both old and new methodologies for defining physicochemical properties, present both in vitro and in vivo methods to test for a variety of immunotoxicities, and describe assays used for pharmacological studies to assess drug release and tissue distribution.

Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design

Artificial intelligence (AI) has emerged as a powerful tool that harnesses anthropomorphic knowledge and provides expedited solutions to complex challenges. Remarkable advancements in AI technology and machine learning present a transformative opportunity in the drug discovery, formulation, and testing of pharmaceutical dosage forms. By utilizing AI algorithms that analyze extensive biological data, including genomics and proteomics, researchers can identify disease-associated targets and predict their interactions with potential drug candidates. This enables a more efficient and targeted approach to drug discovery, thereby increasing the likelihood of successful drug approvals. Furthermore, AI can contribute to reducing development costs by optimizing research and development processes. Machine learning algorithms assist in experimental design and can predict the pharmacokinetics and toxicity of drug candidates. This capability enables the prioritization and optimization of lead compounds, reducing the need for extensive and costly animal testing. Personalized medicine approaches can be facilitated through AI algorithms that analyze real-world patient data, leading to more effective treatment outcomes and improved patient adherence. This comprehensive review explores the wide-ranging applications of AI in drug discovery, drug delivery dosage form designs, process optimization, testing, and pharmacokinetics/pharmacodynamics (PK/PD) studies. This review provides an overview of various AI-based approaches utilized in pharmaceutical technology, highlighting their benefits and drawbacks. Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care.

Role of nanotechnology in the prolonged release of drugs by the subcutaneous route

INTRODUCTION

Subcutaneous physiology is distinct from other parenteral routes that benefit the administration of prolonged-release formulations. A prolonged-release effect is particularly convenient for treating chronic diseases because it is associated with complex and often prolonged posologies. Therefore, drug-delivery systems focused on nanotechnology are proposed as alternatives that can overcome the limitations of current therapeutic regimens and improve therapeutic efficacy.

AREAS COVERED

This review presents an updated systematization of nanosystems, focusing on their applications in highly prevalent chronic diseases. Subcutaneous-delivered nanosystem-based therapies comprehensively summarize nanosystems, drugs, and diseases and their advantages, limitations, and strategies to increase their translation into clinical applications. An outline of the potential contribution of quality-by-design (QbD) and artificial intelligence (AI) to the pharmaceutical development of nanosystems is presented.

EXPERT OPINION

Although recent academic research and development (R&D) advances in the subcutaneous delivery of nanosystems have exhibited promising results, pharmaceutical industries and regulatory agencies need to catch up. The lack of standardized methodologies for analyzing in vitro data from nanosystems for subcutaneous administration and subsequent in vivo correlation limits their access to clinical trials. There is an urgent need for regulatory agencies to develop methods that faithfully mimic subcutaneous administration and specific guidelines for evaluating nanosystems.

Re-envisioning the design of nanomedicines: harnessing automation and artificial intelligence

INTRODUCTION

Interest in nanomedicines has surged in recent years due to the critical role they have played in the COVID-19 pandemic. Nanoformulations can turn promising therapeutic cargo into viable products through improvements in drug safety and efficacy profiles. However, the developmental pathway for such formulations is non-trivial and largely reliant on trial-and-error. Beyond the costly demands on time and resources, this traditional approach may stunt innovation. The emergence of automation, artificial intelligence (AI) and machine learning (ML) tools, which are currently underutilized in pharmaceutical formulation development, offers a promising direction for an improved path in the design of nanomedicines.

AREAS COVERED

the potential of harnessing experimental automation and AI/ML to drive innovation in nanomedicine development. The discussion centers on the current challenges in drug formulation research and development, and the major advantages afforded through the application of data-driven methods.

EXPERT OPINION

The development of integrated workflows based on automated experimentation and AI/ML may accelerate nanomedicine development. A crucial step in achieving this is the generation of high-quality, accessible datasets. Future efforts to make full use of these tools can ultimately contribute to the development of more innovative nanomedicines and improved clinical translation of formulations that rely on advanced drug delivery systems.

Nanomaterials in diagnostics, imaging and delivery: Applications from COVID-19 to cancer

Abstract

In the past two decades, the emergence of nanomaterials for biomedical applications has shown tremendous promise for changing the paradigm of all aspects of disease management. Nanomaterials are particularly attractive for being a modularly tunable system; with the ability to add functionality for early diagnostics, drug delivery, therapy, treatment and monitoring of patient response. In this review, a survey of the landscape of different classes of nanomaterials being developed for applications in diagnostics and imaging, as well as for the delivery of prophylactic vaccines and therapeutics such as small molecules and biologic drugs is undertaken; with a particular focus on COVID-19 diagnostics and vaccination. Work involving bio-templated nanomaterials for high-resolution imaging applications for early cancer detection, as well as for optimal cancer treatment efficacy, is discussed. The main challenges which need to be overcome from the standpoint of effective delivery and mitigating toxicity concerns are investigated. Subsequently, a section is included with resources for researchers and practitioners in nanomedicine, to help tailor their designs and formulations from a clinical perspective. Finally, three key areas for researchers to focus on are highlighted; to accelerate the development and clinical translation of these nanomaterials, thereby unleashing the true potential of nanomedicine in healthcare.

Graphical abstract

Biopredictive tools for the development of injectable drug products

INTRODUCTION

Biopredictive release tests are commonly used in the evaluation of oral medicines. They support decision-making in formulation development and allow predictions of the expected in-vivo performances. So far, there is limited experience in the application of these methodologies to injectable drug products.

AREAS COVERED

Parenteral drug products cover a variety of dosage forms and administration sites including subcutaneous, intramuscular, and intravenous injections. In this area, developing biopredictive and biorelevant methodologies often confronts us with unique challenges and knowledge gaps. Here, we provide a formulation-centric approach and explain the key considerations and workflow when designing biopredictive assays. Also, we outline the key role of computational methods in achieving clinical relevance and put all considerations into context using liposomal nanomedicines as an example.

EXPERT OPINION

Biopredictive tools are the need of the hour to exploit the tremendous opportunities of injectable drug products. A growing number of biopharmaceuticals such as peptides, proteins, and nucleic acids require different strategies and a better understanding of the influences on drug absorption. Here, our design strategy must maintain the balance of robustness and complexity required for effective formulation development.