Synthesis, quality control, and bio-evaluation of 99m Tc-cyclophosphamide

The Chemical Scaffold of Theranostic Radiopharmaceuticals: Radionuclide, Bifunctional Chelator, and Pharmacokinetics Modifying Linker

Therapeutic radiopharmaceuticals have been researched extensively in the last decade as a result of the growing research interest in personalized medicine to improve diagnostic accuracy and intensify intensive therapy while limiting side effects. Radiometal-based drugs are of substantial interest because of their greater versatility for clinical translation compared to non-metal radionuclides. This paper comprehensively discusses various components commonly used as chemical scaffolds to build radiopharmaceutical agents, i.e., radionuclides, pharmacokinetic-modifying linkers, and chelators, whose characteristics are explained and can be used as a guide for the researcher.

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.

Applying Quality by Design Principles in the Development and Preparation of a New Radiopharmaceutical: Technetium-99m-Imatinib Mesylate

The clinical impact and accessibility of ^99mTc tracers for cancer diagnosis would be greatly enhanced by the availability of a new, simple, and easy labeling process and radiopharmaceuticals. In this study, Technetium-99m-imatinib mesylate ([^99mTc]TcIMT) was developed and prepared as a new radiopharmaceutical for breast cancer diagnosis. The effect of critical process parameters on the product quality and stability of [^99mTc]TcIMT was investigated using the quality by design concept of the ICH Q8 (Pharmaceutical Development) guideline. [^99mTc]TcIMT was subjected to in vitro cell binding studies to determine healthy and cancer cell affinity using HaCaT and MCF-7 cells, respectively. The optimal radiolabeling procedure with 1 mg of IMT, 500 μg of stannous chloride, 0.1 mg of ascorbic acid, and ^1mCi ^99mTc radioactivity was obtained for [^99mTc]TcIMT. The pH of the reaction mixture was adjusted to 10 and allowed to react for 15 min at room temperature. The radiochemical purity of [^99mTc]TcIMT was found to be higher than 90% at room temperature up to 6 h. Chromatography analysis revealed >85% [^99mTc]TcIMT complex formation with promising stability in saline, cell medium, and serum up to 6 h. The radiolabeled complex showed a higher cell-binding ratio to MCF-7 cells (88.90% ± 3.12) than HaCaT cells (45.64 ± 4.72) when compared to ^99mTc. Our findings show that the developed preparation method for [^99mTc]TcIMT falls well within the proven acceptable ranges. Applying quality by design (QbD) principles is feasible and worthwhile for the preparation of [^99mTc]TcIMT. In conclusion, radiochemical purity, stability, and in vitro cell binding evaluation of the [^99mTc]TcIMT complex indicate that the agent can be utilized for imaging of breast cancer cells.

Preparation, optimization and pharmacological evaluation of 99mTc- 4-hydroxy-3-(2-hydroxy-3-methoxybenzylideneamino) benzoic acid complex: as a novel potential radiopharmaceutical agent with hepatobiliary excretion

Schiff base ligands are biologically active compounds in having antimicrobial, antiviral and antitumor activities etc. In this study, we have synthesized a Schiff base ligand namely 4-hydroxy-3-(2-hydroxy-3-methoxybenzylideneamino) benzoic acid, by reacting 3-amino-4-hydroxybenzoic acid and 2-hydroxy-3-methoxy benzaldehyde in the presence of acetic acid and refluxing it. The resulting base ligand was characterized on HPLC and used for radiolabelling with technetium-99m. The ligand 4-hydroxy-3-(2-hydroxy-3-methoxybenzylideneamino) benzoic acid was labelled with ^99mTc at pH 7, while reacting 230 µg of ligand with 15 mCi of ^99mTcO^-₄ for 10 min at room temperature. The resulting ^99mTc-ligand complex was characterized by paper chromatography, TLC, HPLC and electrophoresis technique. The stability of the complex was determined at room temperature and in human serum. The biodistribution of the complex was studied in mice and scintigraphy was performed in rabbit. The ^99mTc-ligand complex showed high radiolabelling yield (up to 99 ± 1%) and high stability at room temperature and in human serum. The newly prepared complex exhibited no net charge. Our newly developed ^99mTc-ligand complex demonstrated high accumulation in liver and spleen of mice as well as in rabbit. Based on these findings, we have suggested that this novel radioligand i.e., ^99mTc- 4-hydroxy-3-(2-hydroxy-3-methoxybenzylideneamino) benzoic acid complex could be used for liver and spleen imaging.