Artificial intelligence generates novel 3D printing formulations

Formulation development is a critical step in the development of medicines. The process requires human creativity, ingenuity and in-depth knowledge of formulation development and processing optimization, which can be time-consuming. Herein, we tested the ability of artificial intelligence (AI) to create de novo formulations for three-dimensional (3D) printing. Specifically, conditional generative adversarial networks (cGANs), which are generative models known for their creativity, were trained on a dataset consisting of 1437 fused deposition modelling (FDM) printed formulations that were extracted from both the literature and in-house data. In total, 27 different cGANs architectures were explored with varying learning rate, batch size and number of hidden layers parameters to generate 270 formulations. After a comparison between the characteristics of AI-generated and human-generated formulations, it was discovered that cGANs with a medium learning rate (10−4) could strike a balance in generating formulations that are both novel and realistic. Four of these formulations were fabricated using an FDM printer, of which the first AI-generated formulation was successfully printed. Our study represents a milestone, highlighting the capacity of AI to undertake creative tasks and its potential to revolutionize the drug development process

Virtually Possible: Enhancing Quality Control of 3D-Printed Medicines with Machine Vision Trained on Photorealistic Images

Three-dimensional (3D) printing is an advanced pharmaceutical manufacturing technology, and concerted efforts are underway to establish its applicability to various industries. However, for any technology to achieve widespread adoption, robustness and reliability are critical factors. Machine vision (MV), a subset of artificial intelligence (AI), has emerged as a powerful tool to replace human inspection with unprecedented speed and accuracy. Previous studies have demonstrated the potential of MV in pharmaceutical processes. However, training models using real images proves to be both costly and time consuming. In this study, we present an alternative approach, where synthetic images were used to train models to classify the quality of dosage forms. We generated 200 photorealistic virtual images that replicated 3D-printed dosage forms, where seven machine learning techniques (MLTs) were used to perform image classification. By exploring various MV pipelines, including image resizing and transformation, we achieved remarkable classification accuracies of 80.8%, 74.3%, and 75.5% for capsules, tablets, and films, respectively, for classifying stereolithography (SLA)-printed dosage forms. Additionally, we subjected the MLTs to rigorous stress tests, evaluating their scalability to classify over 3000 images and their ability to handle irrelevant images, where accuracies of 66.5% (capsules), 72.0% (tablets), and 70.9% (films) were obtained. Moreover, model confidence was also measured, and Brier scores ranged from 0.20 to 0.40. Our results demonstrate promising proof of concept that virtual images exhibit great potential for image classification of SLA-printed dosage forms. By using photorealistic virtual images, which are faster and cheaper to generate, we pave the way for accelerated, reliable, and sustainable AI model development to enhance the quality control of 3D-printed medicines

Cold Laser Sintering of Medicines: Toward Carbon Neutral Pharmaceutical Printing

Selective laser sintering (SLS) is an emerging three-dimensional (3D) printing technology that uses a laser to fuse powder particles together, which allows the fabrication of personalized solid dosage forms. It possesses great potential for commercial use. However, a major drawback of SLS is the need to heat the powder bed while printing; this leads to high energy consumption (and hence a large carbon footprint), which may hinder its translation to industry. In this study, the concept of cold laser sintering (CLS) is introduced. In CLS, the aim is to sinter particles without heating the powder bed, where the energy from the laser, alone, is sufficient to fuse adjacent particles. The study demonstrated that a laser power above 1.8 W was sufficient to sinter both KollicoatIR and Eudragit L100-55-based formulations at room temperature. The cold sintering printing process was found to reduce carbon emissions by 99% compared to a commercial SLS printer. The CLS printed formulations possessed characteristics comparable to those made with conventional SLS printing, including a porous microstructure, fast disintegration time, and molecular dispersion of the drug. It was also possible to achieve higher drug loadings than was possible with conventional SLS printing. Increasing the laser power from 1.8 to 3.0 W increased the flexural strength of the printed formulations from 0.6 to 1.6 MPa, concomitantly increasing the disintegration time from 5 to over 300 s. CLS appears to offer a new route to laser-sintered pharmaceuticals that minimizes impact on the environment and is fit for purpose in Industry 5.0

Machine learning using Multi-Modal Data Predicts the Production of Selective Laser Sintered 3D Printed Drug Products

Three-dimensional (3D) printing is drastically redefining medicine production, offering digital precision and personalized design opportunities. One emerging 3D printing technology is selective laser sintering (SLS), which is garnering attention for its high precision, and compatibility with a wide range of pharmaceutical materials, including low-solubility compounds. However, the full potential of SLS for medicines is yet to be realized, requiring expertise and considerable time-consuming and resource-intensive trial-and-error research. Machine learning (ML), a subset of artificial intelligence, is an in silico tool that is accomplishing remarkable breakthroughs in several sectors for its ability to make highly accurate predictions. Therefore, the present study harnessed ML to predict the printability of SLS formulations. Using a dataset of 170 formulations from 78 materials, ML models were developed from inputs that included the formulation composition and characterization data retrieved from Fourier-transformed infrared spectroscopy (FT-IR), X-ray powder diffraction (XRPD) and differential scanning calorimetry (DSC). Multiple ML models were explored, including supervised and unsupervised approaches. The results revealed that ML can achieve high accuracies, by using the formulation composition leading to a maximum F1 score of 81.9%. Using the FT-IR, XRPD and DSC data as inputs resulted in an F1 score of 84.2%, 81.3%, and 80.1%, respectively. A subsequent ML pipeline was built to combine the predictions from FT-IR, XRPD and DSC into one consensus model, where the F1 score was found to further increase to 88.9%. Therefore, it was determined for the first time that ML predictions of 3D printability benefit from multi-modal data, combining numeric, spectral, thermogram and diffraction data. The study lays the groundwork for leveraging existing characterization data for developing high-performing computational models to accelerate developments

3D printed electro-responsive system with programmable drug release

Precision medicine is the next frontier in pharmaceutical research, aiming to improve the safety and efficacy of therapeutics for patients. The ideal drug delivery system (DDS) should be programmable to provide real-time controlled delivery that is personalised to the patient’s needs. However, little progress has been made in this domain. Herein, we combined two cutting-edge technologies, conductive polymers (CPs) and three-dimensional (3D) printing, to demonstrate their potential for achieving programmable controlled release. A DDS was formulated where the CP provided temporal control over drug release. 3D printing was used to ensure dimensional control over the design of the DDS. The CP used in this study is known to be fragile, and thus was blended with thermoplastic polyurethane (TPU) to achieve a conductive elastomer with sound mechanical properties. Rheological and mechanical analyses were performed, where it was revealed that formulation inks with a storage modulus in the order of 103 –104 Pa were both extrudable and maintained their structural integrity. Physicochemical analysis confirmed the presence of the CP functional groups in the 3D printed DDS. Cyclic voltammetry demonstrated that the DDS remained conductive for 100 stimulations. in vitro drug release was performed for 180 min at varying voltages, where a significant difference (p < 0.05) in cumulative release was observed between either ±1.0 V and passive release. Furthermore, the responsiveness of the DDS to pulsatile stimuli was tested, where it was found to rapidly respond to the voltage stimuli, consequently altering the release mechanism. The study is the first to 3D print electroactive medicines using CPs and paves the way for digitalising DDS that can be integrated into the Internet of Things (IoT) framework

Theoretical study of the antioxidant mechanism and structure-activity relationships of 1,3,4-oxadiazol-2-ylthieno[2,3-d]pyrimidin-4-amine derivatives: a computational approach

A theoretical thermodynamic study was conducted to investigate the antioxidant activity and mechanism of 1,3,4-oxadiazol-2-ylthieno[2,3-d]pyrimidin-4-amine derivatives (OTP) using a Density Functional Theory (DFT) approach. The study assessed how solvent environments influence the antioxidant properties of these derivatives. With the increasing prevalence of diseases linked to oxidative stress, such as cancer and cardiovascular diseases, antioxidants are crucial in mitigating the damage caused by free radicals. Previous research has demonstrated the remarkable scavenging abilities of 1,3,4-oxadiazole derivatives, prompting this investigation into their potential using computational methods. DFT calculations were employed to analyze key parameters, including bond dissociation enthalpy (BDE), ionization potential (IP), proton dissociation enthalpy (PDE), and electron transfer enthalpy (ETE), to delineate the antioxidant mechanisms of these compounds. Our findings indicate that specific electron-donating groups such as amine on the phenyl rings significantly enhance the antioxidant activities of these derivatives. The study also integrates global and local reactivity descriptors, such as Fukui functions and HOMO-LUMO energies, to predict the stability and reactivity of these molecules, providing insights into their potential as effective synthetic antioxidants in pharmaceutical applications

3D Printing: Advancements in the Development of Personalised Pharmaceuticals for Older Adults

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1007/978-3-031-35811-1_7Three-dimensional (3D) printing is a revolutionary additive manufacturing technology that constructs objects layer-by-layer. This versatile technology encompasses various processes and possesses unique properties. While originally not intended for this purpose, 3D printing has emerged as a promising approach for personalised pharmaceutical production, specifically Printlets™, which are customised dosage forms. The ageing population presents enduring challenges, and 3D printing offers innovative solutions tailored to this patient subgroup. These solutions encompass a wide range of dosage forms, including multi-drug formulations, orally disintegrating tablets, chewable medicines, multiparticulate systems, and formulations for visually or physically impaired patients. Moreover, besides solid oral dosage forms, 3D printing can also fabricate patient-specific devices for drug delivery via the auricular, topical, intravesical, or vaginal routes. This chapter provides an overview of different 3D printing technologies, highlights the various dosage forms suitable for older patients, and briefly discusses the integration of 3D printing with other advanced digital health technologies

Design and Synthesis of New Thiophene/Thieno[2,3-<i>d</i>]pyrimidines along with Their Cytotoxic Biological Evaluation as Tyrosine Kinase Inhibitors in Addition to Their Apoptotic and Autophagic Induction

This work describes the synthesis and anticancer activity against kinase enzymes of newly designed thiophene and thieno[2,3-d]pyrimidine derivatives, along with their potential to activate autophagic and apoptotic cell death in cancer cells. The designed compounds were scanned for their affinity for kinases. The results were promising with affinity ranges from 46.7% to 13.3%. Molecular docking studies were performed, and the compounds were then screened for their antiproliferative effects. Interestingly, compounds 8 and 5 resulted in higher cytotoxic effects than the reference standard against MCF-7 and HepG-2. The compounds were evaluated for their induction of apoptosis and/or necrosis on HT-29 and HepG-2. Three compounds induced significant early apoptosis compared to untreated control HT-29 cells, and four derivatives were more significant compared to untreated HepG-2 cells. We further investigated the effect of four compounds on the autophagy process within HT-29, HepG-2, and MCF-7 cells with flow cytometry. Similar to the apoptosis results, compound 5 showed the highest autophagic induction among all compounds. The potential inhibitory activity of the synthesized compounds on kinases was assessed. Screened compounds showed inhibition activity ranging from 41.4% to 83.5%. Compounds recorded significant inhibition were further investigated for their specific FLT3 kinase inhibitory activity. Noticeably, Compound 5 exhibited the highest inhibitory activity against FLT3

In-Silico Screening of Novel Synthesized Thienopyrimidines Targeting Fms Related Receptor Tyrosine Kinase-3 and Their In-Vitro Biological Evaluation

The present investigation describes the design strategy and synthesis of novel thienopyrimidine compounds in addition to their anticancer activity targeting tyrosine kinase FLT3 enzyme. The synthesized compounds were subjected to a cytotoxic study where compounds 9a and 9b showed the most potent cytotoxicity against HT-29, HepG-2, and MCF-7 cell lines reflected by their IC50 values for 9a (1.21 ± 0.34, 6.62 ± 0.7 and 7.2 ± 1.9 μM), for 9b (0.85 ± 0.16, 9.11 ± 0.3 and 16.26 ± 2.3 μM) and better than that of reference standard which recorded (1.4 ± 1.16, 13.915 ± 2.2, and 8.43 ± 0.5 μM), respectively. Compounds’ selectivity to malignant cells was determined using selectivity assay, interestingly, all the tested compounds demonstrated an excellent selectivity index (SI) range from 20.2 to 99.7. Target in-silico prediction revealed the FLT3 kinase enzyme was the kinase enzyme of highest probability. Molecular docking studies were performed on the prepared compounds which showed promising binding affinity for FLT3 kinase enzyme and the main interactions between the synthesized ligands and kinase active site were similar to those between the co-crystallized ligand and the receptor. Further biological exploration was performed using in-vitro FLT3 kinase enzyme inhibition assay. The results showed that the 2-morpholinoacetamido derivative 10a exhibited highest FLT3 inhibitory activity among the tested compounds followed by compound 9a then 12. Pharmacokinetic assessment disclosed that all the investigated compounds were considered as “drug-like” molecules with promising bioavailability

Red Beetroot Extract Abrogates Chlorpyrifos-Induced Cortical Damage in Rats

Organophosphorus insecticides including chlorpyrifos (CPF) are mainly used for agriculture, household, and military purposes; their application is associated with various adverse reactions in animals and humans. This study was conducted to evaluate the potential neuroprotective effect of red beetroot methanolic extract (RBR) against CPF-induced cortical damage. Twenty-eight adult male Wistar albino rats were divided into 4 groups (n=7 in each group): the control group was administered physiological saline (0.9% NaCl), the CPF group was administered CPF (10 mg/kg), the RBR group was administered RBR (300 mg/kg), and the RBR+CPF group was treated with RBR (300 mg/kg) 1 hr before CPF (10 mg/kg) supplementation. All groups were treated for 28 days. Rats exposed to CPF exhibited a significant decrease in cortical acetylcholinesterase activity and brain-derived neurotrophic factor and a decrease in glial fibrillary acidic protein. CPF intoxication increased lipid peroxidation, inducible nitric oxide synthase expression, and nitric oxide production. This was accompanied by a decrease in glutathione content and in the activities of glutathione peroxidase, glutathione reductase, superoxide dismutase, and catalase in the cortical tissue. Additionally, CPF enhanced inflammatory response, indicated by increased levels and expression of interleukin-1β and tumor necrosis factor-α. CPF triggered neuronal apoptosis by upregulating Bax and caspase-3 and downregulating Bcl-2. However, RBR reversed the induced neuronal alterations following CPF intoxication. Our findings suggest that RBR can minimize and prevent CPF neurotoxicity through its antioxidant, anti-inflammatory, and antiapoptotic activities