Robotics, microfluidics, nanotechnology and AI in the synthesis and evaluation of liposomes and polymeric drug delivery systems

Emerging Perspectives on Prime Editor Delivery to the Brain

Prime editing shows potential as a precision genome editing technology, as well as the potential to advance the development of next-generation nanomedicine for addressing neurological disorders. However, turning in prime editors (PEs), which are macromolecular complexes composed of CRISPR/Cas9 nickase fused with a reverse transcriptase and a prime editing guide RNA (pegRNA), to the brain remains a considerable challenge due to physiological obstacles, including the blood-brain barrier (BBB). This review article offers an up-to-date overview and perspective on the latest technologies and strategies for the precision delivery of PEs to the brain and passage through blood barriers. Furthermore, it delves into the scientific significance and possible therapeutic applications of prime editing in conditions related to neurological diseases. It is targeted at clinicians and clinical researchers working on advancing precision nanomedicine for neuropathologies.

Micro/Nanorobotics in In Vitro Fertilization: A Paradigm Shift in Assisted Reproductive Technologies

In vitro fertilization (IVF) has transformed the sector of assisted reproductive technology (ART) by presenting hope to couples facing infertility challenges. However, conventional IVF strategies include their own set of problems such as success rates, invasive procedures, and ethical issues. The integration of micro/nanorobotics into IVF provides a prospect to address these challenging issues. This article provides an outline of the use of micro/nanorobotics in IVF specializing in advancing sperm manipulation, egg retrieval, embryo culture, and capacity future improvements in this swiftly evolving discipline. The article additionally explores the challenges and obstacles associated with the integration of micro/nanorobotics into IVF, in addition to the ethical concerns and regulatory elements related to the usage of advanced technologies in ART. A comprehensive discussion of the risk and safety considerations related to using micro/nanorobotics in IVF techniques is likewise presented. Through this exploration, we delve into the core principles, benefits, challenges, and potential impact of micro/nanorobotics in revolutionizing IVF procedures and enhancing affected person outcomes.

Advancing genome editing with artificial intelligence: opportunities, challenges, and future directions

Clustered regularly interspaced short palindromic repeat (CRISPR)-based genome editing (GED) technologies have unlocked exciting possibilities for understanding genes and improving medical treatments. On the other hand, Artificial intelligence (AI) helps genome editing achieve more precision, efficiency, and affordability in tackling various diseases, like Sickle cell anemia or Thalassemia. AI models have been in use for designing guide RNAs (gRNAs) for CRISPR-Cas systems. Tools like DeepCRISPR, CRISTA, and DeepHF have the capability to predict optimal guide RNAs (gRNAs) for a specified target sequence. These predictions take into account multiple factors, including genomic context, Cas protein type, desired mutation type, on-target/off-target scores, potential off-target sites, and the potential impacts of genome editing on gene function and cell phenotype. These models aid in optimizing different genome editing technologies, such as base, prime, and epigenome editing, which are advanced techniques to introduce precise and programmable changes to DNA sequences without relying on the homology-directed repair pathway or donor DNA templates. Furthermore, AI, in collaboration with genome editing and precision medicine, enables personalized treatments based on genetic profiles. AI analyzes patients' genomic data to identify mutations, variations, and biomarkers associated with different diseases like Cancer, Diabetes, Alzheimer's, etc. However, several challenges persist, including high costs, off-target editing, suitable delivery methods for CRISPR cargoes, improving editing efficiency, and ensuring safety in clinical applications. This review explores AI's contribution to improving CRISPR-based genome editing technologies and addresses existing challenges. It also discusses potential areas for future research in AI-driven CRISPR-based genome editing technologies. The integration of AI and genome editing opens up new possibilities for genetics, biomedicine, and healthcare, with significant implications for human health.

Turning catalytically active pores into active pumps

We develop a semi-analytical model of self-diffusioosmotic transport in active pores, which includes advective transport and the inverse chemical reaction that consumes solute. In previous work [Antunes et al., Phys. Rev. Lett. 129, 188003 (2022)], we have demonstrated the existence of a spontaneous symmetry breaking in fore-aft symmetric pores that enables them to function as a micropump. We now show that this pumping transition is controlled by three timescales. Two timescales characterize advective and diffusive transport. The third timescale corresponds to how long a solute molecule resides in the pore before being consumed. Introducing asymmetry to the pore (either via the shape or the catalytic coating) reveals a second type of advection-enabled transition. In asymmetric pores, the flow rate exhibits discontinuous jumps and hysteresis loops upon tuning the parameters that control the asymmetry. This work demonstrates the interconnected roles of shape and catalytic patterning in the dynamics of active pores and shows how to design a pump for optimum performance.

Artificial Intelligence's Impact on Drug Discovery and Development From Bench to Bedside

Artificial intelligence (AI) techniques have the potential to revolutionize drug release modeling, optimize therapy for personalized medicine, and minimize side effects. By applying AI algorithms, researchers can predict drug release profiles, incorporate patient-specific factors, and optimize dosage regimens to achieve tailored and effective therapies. This AI-based approach has the potential to improve treatment outcomes, enhance patient satisfaction, and advance the field of pharmaceutical sciences. International collaborations and professional organizations play vital roles in establishing guidelines and best practices for data collection and sharing. Open data initiatives can enhance transparency and scientific progress, facilitating algorithm validation.

Knowledge Gaps in Generating Cell-Based Drug Delivery Systems and a Possible Meeting with Artificial Intelligence

Cell-based drug delivery systems are new strategies in targeted delivery in which cells or cell-membrane-derived systems are used as carriers and release their cargo in a controlled manner. Recently, great attention has been directed to cells as carrier systems for treating several diseases. There are various challenges in the development of cell-based drug delivery systems. The prediction of the properties of these platforms is a prerequisite step in their development to reduce undesirable effects. Integrating nanotechnology and artificial intelligence leads to more innovative technologies. Artificial intelligence quickly mines data and makes decisions more quickly and accurately. Machine learning as a subset of the broader artificial intelligence has been used in nanomedicine to design safer nanomaterials. Here, how challenges of developing cell-based drug delivery systems can be solved with potential predictive models of artificial intelligence and machine learning is portrayed. The most famous cell-based drug delivery systems and their challenges are described. Last but not least, artificial intelligence and most of its types used in nanomedicine are highlighted. The present Review has shown the challenges of developing cells or their derivatives as carriers and how they can be used with potential predictive models of artificial intelligence and machine learning.

Establishing and testing a robot-based platform to enable the automated production of nanoparticles in a flexible and modular way

Robotic systems facilitate relatively simple human-robot interaction for non-robot experts, providing the flexibility to implement different processes. In this context, shorter process times, as well as an increased product and process quality could be achieved. Robots short time-consuming processes, take over ergonomically unfavorable tasks and work efficiently all the time. In addition, flexible production is possible while maintaining or even increasing safety. This study describes the successful development of a dual-arm robot-based modular infrastructure and the establishment of an automated process for the reproducible production of nanoparticles. As proof of concept, a manual synthesis protocol for silica nanoparticle preparation with a diameter of about 200 nm as building blocks for photonic crystals was translated into a fully automated process. All devices and components of the automated system were optimized and adapted according to the synthesis requirements. To demonstrate the benefit of the automated nanoparticle production, manual (synthesis done by lab technicians) and automated syntheses were benchmarked. To this end, different processing parameters (time of synthesis procedure, accuracy of dosage etc.) and the properties of the produced nanoparticles were compared. We demonstrate that the use of the robot not only increased the synthesis accuracy and reproducibility but reduced the personnel time and costs up to 75%.

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.

Thermal and Dielectric Investigations of Polystyrene Nanoparticles as a Viable Platform-Toward the Next Generation of Fillers for Nanocomposites

Nanoparticles are often used as fillers for enhancing various properties of polymer composites such as mechanical, electrical, or dielectric. Among them, polymer nanoparticles are considered ideal contenders because of their compatibility with a polymer matrix. For this reason, it is important that they are synthesized in a surfactant-free form, to obtain predictable surface and structural properties. Here, we synthesized a series of polystyrene nanoparticles (PS NPs), by emulsion polymerization of styrene, using varying amounts of divinylbenzene as a crosslinking agent and sodium 4-vinylbenzenesulfonate as a copolymerizing monomer surfactant-"surfmer". Using "surfmers" we obtained surfactant-free nanoparticles that are monodisperse, with a high degree of thermal stability, as observed by scanning electron microscopy and thermogravimetric investigations. The prepared series of NPs were investigated by means of broadband dielectric spectroscopy and we demonstrate that by fine-tuning their chemical composition, fine changes in their dielectric and thermal properties are obtained. Further, we demonstrate that the physical transformations in the nanoparticles, such as the glass transition, can be predicted by performing the first derivative of dielectric permittivity for all investigated samples. The glass transition temperature of PS NPs appears to be inversely correlated with the dielectric permittivity and the average diameter of NPs.

A machine learning and explainable artificial intelligence triage-prediction system for COVID-19

COVID-19 is a respiratory disease caused by the SARS-CoV-2 contagion, severely disrupted the healthcare infrastructure. Various countries have developed COVID-19 vaccines that have effectively prevented the severe symptoms caused by the virus to a certain extent. However, a small section of people continues to perish. Artificial intelligence advances have revolutionized healthcare diagnosis and prognosis infrastructure. In this study, we predict the severity of COVID-19 using heterogenous Machine Learning and Deep Learning algorithms by considering clinical markers, vital signs, and other critical factors. This study extensively reviews various classifier architectures to predict the COVID-19 severity. We built and evaluated multiple pipelines entailing combinations of five state-of-the-art data-balancing techniques (Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic, Borderline SMOTE, SMOTE with Tomek links, and SMOTE with Edited Nearest Neighbor (ENN)) and twelve heterogeneous classifiers such as Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, Naïve Bayes, Xgboost, Extratrees, Adaboost, Light GBM, Catboost, and 1-D Convolution Neural Network. The best-performing pipeline consists of Random Forest trained on Borderline SMOTE balanced data that produced the highest recall of 83%. We deployed Explainable Artificial Intelligence tools such as Shapley Additive Explanations and Local Interpretable Model-agnostic Explanations, ELI5, Qlattice, Anchor, and Feature Importance to demystify complex tree-based ensemble models. These tools provide valuable insights into the significance of critical features in the severity prediction of a COVID-19 patient. It was observed that changes in respiratory rate, blood pressure, lactate, and calcium values were the primary contributors to the increase in severity of a COVID-19 patient. This architecture aims to be an explainable decision-support triaging system for medical professionals in countries lacking advanced medical technology and infrastructure to reduce fatalities.

Ethical and legal challenges in nanomedical innovations: a scoping review

Background: Rapid advancements in research and development related to nanomedical technology raise various ethical and legal challenges in areas relevant to disease detection, diagnosis, and treatment. This study aims to outline the existing literature, covering issues associated with emerging nanomedicine and related clinical research, and identify implications for the responsible advancement and integration of nanomedicine and nanomedical technology throughout medical networks in the future. Methods: A scoping review, designed to cover scientific, ethical, and legal literature associated with nanomedical technology, was conducted, generating and analyzing 27 peer-reviewed articles published between 2007-2020. Results: Results indicate that articles referencing ethical and legal issues related to nanomedical technology were concerned with six key areas: 1) harm exposure and potential risks to health, 2) consent to nano-research, 3) privacy, 4) access to nanomedical technology and potential nanomedical therapies, 5) classification of nanomedical products in relation to the research and development of nanomedical technology, and 6) the precautionary principle as it relates to the research and development of nanomedical technology. Conclusion: This review of the literature suggests that few practical solutions are comprehensive enough to allay the ethical and legal concerns surrounding research and development in fields related to nanomedical technology, especially as it continues to evolve and contribute to future innovations in medicine. It is also clearly apparent that a more coordinated approach is required to ensure global standards of practice governing the study and development of nanomedical technology, especially as discussions surrounding the regulation of nanomedical research throughout the literature are mainly confined to systems of governance in the United States.

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.

Microfluidic-based technologies for diagnosis, prevention, and treatment of COVID-19: recent advances and future directions

The COVID-19 pandemic has posed significant challenges to existing healthcare systems around the world. The urgent need for the development of diagnostic and therapeutic strategies for COVID-19 has boomed the demand for new technologies that can improve current healthcare approaches, moving towards more advanced, digitalized, personalized, and patient-oriented systems. Microfluidic-based technologies involve the miniaturization of large-scale devices and laboratory-based procedures, enabling complex chemical and biological operations that are conventionally performed at the macro-scale to be carried out on the microscale or less. The advantages microfluidic systems offer such as rapid, low-cost, accurate, and on-site solutions make these tools extremely useful and effective in the fight against COVID-19. In particular, microfluidic-assisted systems are of great interest in different COVID-19-related domains, varying from direct and indirect detection of COVID-19 infections to drug and vaccine discovery and their targeted delivery. Here, we review recent advances in the use of microfluidic platforms to diagnose, treat or prevent COVID-19. We start by summarizing recent microfluidic-based diagnostic solutions applicable to COVID-19. We then highlight the key roles microfluidics play in developing COVID-19 vaccines and testing how vaccine candidates perform, with a focus on RNA-delivery technologies and nano-carriers. Next, microfluidic-based efforts devoted to assessing the efficacy of potential COVID-19 drugs, either repurposed or new, and their targeted delivery to infected sites are summarized. We conclude by providing future perspectives and research directions that are critical to effectively prevent or respond to future pandemics.

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.

The Application of Artificial Intelligence in Magnetic Hyperthermia Based Research

The development of nanomedicine involves complex nanomaterial research involving magnetic nanomaterials and their use in magnetic hyperthermia. The selection of the optimal treatment strategies is time-consuming, expensive, unpredictable, and not consistently effective. Delivering personalized therapy that obtains maximal efficiency and minimal side effects is highly important. Thus, Artificial Intelligence (AI) based algorithms provide the opportunity to overcome these crucial issues. In this paper, we briefly overview the significance of the combination of AI-based methods, particularly the Machine Learning (ML) technique, with magnetic hyperthermia. We considered recent publications, reports, protocols, and review papers from Scopus and Web of Science Core Collection databases, considering the PRISMA-S review methodology on applying magnetic nanocarriers in magnetic hyperthermia. An algorithmic performance comparison in terms of their types and accuracy, data availability taking into account their amount, types, and quality was also carried out. Literature shows AI support of these studies from the physicochemical evaluation of nanocarriers, drug development and release, resistance prediction, dosing optimization, the combination of drug selection, pharmacokinetic profile characterization, and outcome prediction to the heat generation estimation. The papers reviewed here clearly illustrate that AI-based solutions can be considered as an effective supporting tool in drug delivery, including optimization and behavior of nanocarriers, both in vitro and in vivo, as well as the delivery process. Moreover, the direction of future research, including the prediction of optimal experiments and data curation initiatives has been indicated.

Pumping and Mixing in Active Pores

We show both numerically and analytically that a chemically patterned active pore can act as a micro- or nanopump for fluids, even if it is fore-aft symmetric. This is possible due to a spontaneous symmetry breaking which occurs when advection rather than diffusion is the dominant mechanism of solute transport. We further demonstrate that, for pumping and tuning the flow rate, a combination of geometrical and chemical inhomogeneities is required. For certain parameter values, the flow is unsteady, and persistent oscillations with a tunable frequency appear. Finally, we find that the flow exhibits convection rolls and hence promotes mixing in the low Reynolds number regime.

Phase Separation in Liposomes Determined by Ergosterol and Classified Using Machine Learning

Recent studies indicated that ergosterol (Erg) helps form strongly ordered lipid domains in membranes that depend on their chemical characters. However, direct evidence of concentration-dependent interaction of Erg with lipid membranes has not been reported. We studied the Erg concentration-dependent changes in the phase behaviors of membranes using cell-sized liposomes containing 1,2-Dioleoyl-sn-glycero-3-phosphocholine (DOPC)/1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC). We observed the concentration range of phase separation in ternary membranes was significantly wider when Erg rather than cholesterol (Chol) was used as the sterol component. We used machine learning for the first time to analyze microscopic images of cell-sized liposomes and identify phase-separated structures. The automated method was successful in identifying homogeneous membranes but performance remained data-limited for the identification of phase separation domains characterized by more complex features.

Microfluidic Organ-on-a-Chip System for Disease Modeling and Drug Development

An organ-on-a-chip is a device that combines micro-manufacturing and tissue engineering to replicate the critical physiological environment and functions of the human organs. Therefore, it can be used to predict drug responses and environmental effects on organs. Microfluidic technology can control micro-scale reagents with high precision. Hence, microfluidics have been widely applied in organ-on-chip systems to mimic specific organ or multiple organs in vivo. These models integrated with various sensors show great potential in simulating the human environment. In this review, we mainly introduce the typical structures and recent research achievements of several organ-on-a-chip platforms. We also discuss innovations in models applied to the fields of pharmacokinetics/pharmacodynamics, nano-medicine, continuous dynamic monitoring in disease modeling, and their further applications in other fields.

Intelligent control of nanoparticle synthesis through machine learning

The synthesis of nanoparticles is affected by many reaction conditions, and their properties are usually determined by factors such as their size, shape and surface chemistry. In order for the synthesized nanoparticles to have functions suitable for different fields (for example, optics, electronics, sensor applications and so on), precise control of their properties is essential. However, with the current technology of preparing nanoparticles on a microreactor, it is time-consuming and laborious to achieve precise synthesis. In order to improve the efficiency of synthesizing nanoparticles with the expected functionality, the application of machine learning-assisted synthesis is an intelligent choice. In this article, we mainly introduce the typical methods of preparing nanoparticles on microreactors, and explain the principles and procedures of machine learning, as well as the main ways of obtaining data sets. We have studied three types of representative nanoparticle preparation methods assisted by machine learning. Finally, the current problems in machine learning-assisted nanoparticle synthesis and future development prospects are discussed.

Microfluidic fabrication of lipid nanoparticles for the delivery of nucleic acids

Gene therapy has emerged as a potential platform for treating several dreaded and rare diseases that would not have been possible with traditional therapies. Viral vectors have been widely explored as a key platform for gene therapy due to their ability to efficiently transport nucleic acid-based therapeutics into the cells. However, the lack of precision in their delivery has led to several off-target toxicities. As such, various strategies in the form of non-viral gene delivery vehicles have been explored and are currenlty employed in several therapies including the SARS-CoV-2 vaccine. In this review, we discuss the opportunities lipid nanoparticles (LNPs) present for efficient gene delivery. We also discuss various synthesis strategies via microfluidics for high throughput fabrication of non-viral gene delivery vehicles. We conclude with the recent applications and clinical trials of these vehicles for the delivery of different genetic materials such as CRISPR editors and RNA for different medical conditions ranging from cancer to rare diseases.

Nanocarrier Drug Delivery Systems: Characterization, Limitations, Future Perspectives and Implementation of Artificial Intelligence

There has been an increasing demand for the development of nanocarriers targeting multiple diseases with a broad range of properties. Due to their tiny size, giant surface area and feasible targetability, nanocarriers have optimized efficacy, decreased side effects and improved stability over conventional drug dosage forms. There are diverse types of nanocarriers that have been synthesized for drug delivery, including dendrimers, liposomes, solid lipid nanoparticles, polymersomes, polymer-drug conjugates, polymeric nanoparticles, peptide nanoparticles, micelles, nanoemulsions, nanospheres, nanocapsules, nanoshells, carbon nanotubes and gold nanoparticles, etc. Several characterization techniques have been proposed and used over the past few decades to control and predict the behavior of nanocarriers both in vitro and in vivo. In this review, we describe some fundamental in vitro, ex vivo, in situ and in vivo characterization methods for most nanocarriers, emphasizing their advantages and limitations, as well as the safety, regulatory and manufacturing aspects that hinder the transfer of nanocarriers from the laboratory to the clinic. Moreover, integration of artificial intelligence with nanotechnology, as well as the advantages and problems of artificial intelligence in the development and optimization of nanocarriers, are also discussed, along with future perspectives.

Merging data curation and machine learning to improve nanomedicines

Nanomedicine design is often a trial-and-error process, and the optimization of formulations and in vivo properties requires tremendous benchwork. To expedite the nanomedicine research progress, data science is steadily gaining importance in the field of nanomedicine. Recently, efforts have explored the potential to predict nanomaterials synthesis and biological behaviors via advanced data analytics. Machine learning algorithms process large datasets to understand and predict various material properties in nanomedicine synthesis, pharmacologic parameters, and efficacy. "Big data" approaches may enable even larger advances, especially if researchers capitalize on data curation methods. However, the concomitant use of data curation processes needed to facilitate the acquisition and standardization of large, heterogeneous data sets, to support advanced data analytics methods such as machine learning has yet to be leveraged. Currently, data curation and data analytics areas of nanotechnology-focused data science, or 'nanoinformatics', have been proceeding largely independently. This review highlights the current efforts in both areas and the potential opportunities for coordination to advance the capabilities of data analytics in nanomedicine.

Nano-engineered tools in the diagnosis, therapeutics, prevention, and mitigation of SARS-CoV-2

The recent outbreak of SARS-CoV-2 has forever altered mankind resulting in the COVID-19 pandemic. This respiratory virus further manifests into vital organ damage, resulting in severe post COVID-19 complications. Nanotechnology has been moonlighting in the scientific community to combat several severe diseases. This review highlights the triune of the nano-toolbox in the areas of diagnostics, therapeutics, prevention, and mitigation of SARS-CoV-2. Nanogold test kits have already been on the frontline of rapid detection. Breath tests, magnetic nanoparticle-based nucleic acid detectors, and the use of Raman Spectroscopy present myriads of possibilities in developing point of care biosensors, which will ensure sensitive, affordable, and accessiblemass surveillance. Most of the therapeutics are trying to focus on blocking the viral entry into the cell and fighting with cytokine storm, using nano-enabled drug delivery platforms. Nanobodies and mRNA nanotechnology with lipid nanoparticles (LNPs) as vaccines against S and N protein have regained importance. All the vaccines coming with promising phase 3 clinical trials have used nano-delivery systems for delivery of vaccine-cargo, which are currently administered widely in many countries. The use of chemically diverse metal, carbon and polymeric nanoparticles, nanocages and nanobubbles demonstrate opportunities to develop anti-viral nanomedicine. In order to prevent and mitigate the viral spread, high-performance charged nanofiber filters, spray coating of nanomaterials on surfaces, novel materials for PPE kits and facemasks have been developed that accomplish over 90% capture of airborne SARS-CoV-2. Nano polymer-based disinfectants are being tested to make smart-transport for human activities. Despite the promises of this toolbox, challenges in terms of reproducibility, specificity, efficacy and emergence of new SARS-CoV-2 variants are yet to overcome.

Advanced human-relevant in vitro pulmonary platforms for respiratory therapeutics

Over the past years, advanced in vitro pulmonary platforms have witnessed exciting developments that are pushing beyond traditional preclinical cell culture methods. Here, we discuss ongoing efforts in bridging the gap between in vivo and in vitro interfaces and identify some of the bioengineering challenges that lie ahead in delivering new generations of human-relevant in vitro pulmonary platforms. Notably, in vitro strategies using foremost lung-on-chips and biocompatible "soft" membranes have focused on platforms that emphasize phenotypical endpoints recapitulating key physiological and cellular functions. We review some of the most recent in vitro studies underlining seminal therapeutic screens and translational applications and open our discussion to promising avenues of pulmonary therapeutic exploration focusing on liposomes. Undeniably, there still remains a recognized trade-off between the physiological and biological complexity of these in vitro lung models and their ability to deliver assays with throughput capabilities. The upcoming years are thus anticipated to see further developments in broadening the applicability of such in vitro systems and accelerating therapeutic exploration for drug discovery and translational medicine in treating respiratory disorders.

Patentability challenges associated with emerging pharmaceutical technologies

According to the recent patent filing trends, it has been observed that certain pharmaceutical technologies are more popular than others and are commonly referred to as emerging technologies. The emerging technologies in the pharmaceutical sector include artificial intelligence, big data and certain advanced biological therapies such as personalized medicine and stem cell therapy. These trends have various applications in the medicine and healthcare industry. Since these technologies are relatively new and each of them is very unique in its own way, current patent laws are inadequate to deal with the complex challenges associated with them. A brief analysis of the challenges associated with these emerging technologies and their applications is discussed in this paper.

Towards proteinoid computers. Hypothesis paper

Proteinoids - thermal proteins - are produced by heating amino acids to their melting point and initiation of polymerisation to produce polymeric chains. Proteinoids swell in aqueous solution into hollow microspheres. The proteinoid microspheres produce endogenous burst of electrical potential spikes and change patterns of their electrical activity in response to illumination. The microspheres can interconnect by pores and tubes and form networks with a programmable growth. We speculate on how ensembles of the proteinoid microspheres can be developed into unconventional computing devices.

Hydrotalcite-Niclosamide Nanohybrid as Oral Formulation towards SARS-CoV-2 Viral Infections

COVID-19 has been affecting millions of individuals worldwide and, thus far, there is no accurate therapeutic strategy. This critical situation necessitates novel formulations for already existing, FDA approved, but poorly absorbable drug candidates, such as niclosamide (NIC), which is of great relevance. In this context, we have rationally designed NIC-loaded hydrotalcite composite nanohybrids, which were further coated with Tween 60 or hydroxypropyl methyl cellulose (HPMC), and characterized them in vitro. The optimized nanohybrids showed particle sizes <300 nm and were orally administrated to rats to determine whether they could retain an optimum plasma therapeutic concentration of NIC that would be effective for treating COVID-19. The pharmacokinetic (PK) results clearly indicated that hydrotalcite-based NIC formulations could be highly potential options for treating the ongoing pandemic and we are on our way to understanding the in vivo anti-viral efficacy sooner. It is worth mentioning that hydrotalcite–NIC nanohybrids maintained a therapeutic NIC level, even above the required IC₅₀ value, after just a single administration in 8–12 h. In conclusion, we were very successfully able to develop a NIC oral formulation by immobilizing with hydrotalcite nanoparticles, which were further coated with Tween 60 or HPMC, in order to enhance their emulsification in the gastrointestinal tract.

Review of machine learning algorithms' application in pharmaceutical technology

Machine learning algorithms, and artificial intelligence in general, have a wide range of applications in the field of pharmaceutical technology. Starting from the formulation development, through a great potential for integration within the Quality by design framework, these data science tools provide a better understanding of the pharmaceutical formulations and respective processing. Machine learning algorithms can be especially helpful with the analysis of the large volume of data generated by the Process analytical technologies. This paper provides a brief explanation of the artificial neural networks, as one of the most frequently used machine learning algorithms. The process of the network training and testing is described and accompanied with illustrative examples of machine learning tools applied in the context of pharmaceutical formulation development and related technologies, as well as an overview of the future trends. Recently published studies on more sophisticated methods, such as deep neural networks and light gradient boosting machine algorithm, have been described. The interested reader is also referred to several official documents (guidelines) that pave the way for a more structured representation of the machine learning models in their prospective submissions to the regulatory bodies.

Development of Lipid Nanoparticles for the Delivery of Macromolecules Based on the Molecular Design of pH-Sensitive Cationic Lipids

Considerable efforts have been made on the development of lipid nanoparticles (LNPs) for delivering of nucleic acids in LNP-based medicines, including a first-ever short interfering RNA (siRNA) medicine, Onpattro, and the mRNA vaccines against the coronavirus disease 2019 (COVID-19), which have been approved and are currently in use worldwide. The successful rational design of ionizable cationic lipids was a major breakthrough that dramatically increased delivery efficiency in this field. The LNPs would be expected to be useful as a platform technology for the delivery of various therapeutic modalities for genome editing and even for undiscovered therapeutic mechanisms. In this review, the current progress of my research, including the molecular design of pH-sensitive cationic lipids, their applications for various tissues and cell types, and for delivering various macromolecules, including siRNA, antisense oligonucleotide, mRNA, and the clustered regularly interspaced short palindromic repeats (CRISPR)-associated (Cas) system will be described. Mechanistic studies regarding relationships between the physicochemical properties of LNPs, drug delivery, and biosafety are also summarized. Furthermore, current issues that need to be addressed for next generation drug delivery systems are discussed.