Multi-Step Usage of in Vivo Models During Rational Drug Design and Discovery

More than a dozen enzymes have been found to be activated or inhibited in vitro by disulfide-exchange between the protein and small-molecule disulfides. Accordingly, thiol/disulfide ratio changes in vivo may be of great importance in the regulation of cellular metabolism. An awareness of this regulatory mechanism in both host cells and parasites, coupled with information on the presence or absence of key enzymes, may lead to rational drug design against certain diseases involving thiol intermediates, including trypanosomiasis.

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From serendipity to rational drug design in brain disorders: in silico, in vitro, and in vivo approaches

Current Opinion in Pharmacology ◽

10.1016/j.coph.2021.07.012 ◽

2021 ◽

Vol 60 ◽

pp. 177-182

Author(s):

Hyunjung Oh ◽

Thomas D. Prevot ◽

Dwight Newton ◽

Etienne Sibille

Keyword(s):

Drug Design ◽

In Silico ◽

Rational Drug Design ◽

Brain Disorders ◽

Rational Drug

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Rational Drug Design of Targeted and Enzyme Cleavable Vitamin E Analogs as Neoadjuvant to Chemotherapy: In Vitro and In Vivo Evaluation on Reduction of Cardiotoxicity of Doxorubicin.

10.1101/2021.05.20.445072 ◽

2021 ◽

Author(s):

Raghu S Pandurangi ◽

Orsolya Cseh ◽

Artee Luchman ◽

siguang Xu ◽

Cynthia Ma ◽

...

Keyword(s):

Vitamin E ◽

Drug Design ◽

Cancer Cells ◽

Water Solubility ◽

Therapeutic Index ◽

Rational Drug Design ◽

In Vivo Evaluation ◽

Rational Drug

Traditional drug design focus on specific target (s) expressed by cancer cells. However, cancer cells outsmart the interventions by activating survival pathways and/or downregulating cell death pathways. As the research in molecular biology of cancer grows exponentially, new methods of drug designs are needed to target multiple pathways/targets which are involved in survival of cancer cells. Vitamin E analogues including a-tocopheryl succinate (TOS) is a well-known anti-tumoregenic agent which is well studied both in vitro and in vivo tumor models. However, lack of targeting cancer cells and unexpected toxicity along with the poor water solubility of TOS compelled a rational drug design using both targeting and cleavable technologies incorporated in the new drug design. A plethora of Vitamin E derivatives (AMP-001, 002 and 003) were synthesized, characterized and studied for the improved efficacy and lowered toxicity in various cancer cells in vitro. Preliminary studies revealed AAAPT leading candidates reduced the invasive potential of brain tumor stem cells, synergized with different drugs and different treatments. AAAPT leading drug AMP-001 enhanced the therapeutic index of front-line drug Doxorubicin in triple negative breast cancer (TNBC) tumor rat model preserving the ventricular function when used as a neoadjuvant to Doxorubicin. These results may pave the way for reducing the cardiotoxicity of chemotherapy in clinical settings.

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Improving the Accuracy of Protein-Ligand Binding Affinity Prediction by Deep Learning Models: Benchmark and Model

10.26434/chemrxiv.9866912 ◽

2019 ◽

Author(s):

Mohammad Rezaei ◽

Yanjun Li ◽

Xiaolin Li ◽

Chenglong Li

Keyword(s):

Deep Learning ◽

Drug Design ◽

Binding Affinity ◽

Benchmark Dataset ◽

Rational Drug Design ◽

Learning Models ◽

Structure Based Drug Design ◽

Binding Affinity Prediction ◽

Affinity Prediction ◽

Rational Drug

Introduction: The ability to discriminate among ligands binding to the same protein target in terms of their relative binding affinity lies at the heart of structure-based drug design. Any improvement in the accuracy and reliability of binding affinity prediction methods decreases the discrepancy between experimental and computational results. Objectives: The primary objectives were to find the most relevant features affecting binding affinity prediction, least use of manual feature engineering, and improving the reliability of binding affinity prediction using efficient deep learning models by tuning the model hyperparameters. Methods: The binding site of target proteins was represented as a grid box around their bound ligand. Both binary and distance-dependent occupancies were examined for how an atom affects its neighbor voxels in this grid. A combination of different features including ANOLEA, ligand elements, and Arpeggio atom types were used to represent the input. An efficient convolutional neural network (CNN) architecture, DeepAtom, was developed, trained and tested on the PDBbind v2016 dataset. Additionally an extended benchmark dataset was compiled to train and evaluate the models. Results: The best DeepAtom model showed an improved accuracy in the binding affinity prediction on PDBbind core subset (Pearson’s R=0.83) and is better than the recent state-of-the-art models in this field. In addition when the DeepAtom model was trained on our proposed benchmark dataset, it yields higher correlation compared to the baseline which confirms the value of our model. Conclusions: The promising results for the predicted binding affinities is expected to pave the way for embedding deep learning models in virtual screening and rational drug design fields.

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Rational Drug Design Approach of Receptor Tyrosine Kinase Type III Inhibitors

Current Medicinal Chemistry ◽

10.2174/0929867325666180622143548 ◽

2020 ◽

Vol 26 (42) ◽

pp. 7623-7640 ◽

Cited By ~ 1

Author(s):

Cheolhee Kim ◽

Eunae Kim

Keyword(s):

Drug Design ◽

Tyrosine Kinase ◽

Receptor Tyrosine Kinase ◽

Synergistic Effects ◽

Rational Drug Design ◽

Computational Techniques ◽

Biological Targets ◽

Type Iii ◽

Theoretical Approaches ◽

Rational Drug

: Rational drug design is accomplished through the complementary use of structural biology and computational biology of biological macromolecules involved in disease pathology. Most of the known theoretical approaches for drug design are based on knowledge of the biological targets to which the drug binds. This approach can be used to design drug molecules that restore the balance of the signaling pathway by inhibiting or stimulating biological targets by molecular modeling procedures as well as by molecular dynamics simulations. Type III receptor tyrosine kinase affects most of the fundamental cellular processes including cell cycle, cell migration, cell metabolism, and survival, as well as cell proliferation and differentiation. Many inhibitors of successful rational drug design show that some computational techniques can be combined to achieve synergistic effects.

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Recent Advances in the Rational Drug Design Based on Multi-target Ligands

Current Medicinal Chemistry ◽

10.2174/0929867327666200102120652 ◽

2020 ◽

Vol 27 (28) ◽

pp. 4720-4740 ◽

Cited By ~ 3

Author(s):

Ting Yang ◽

Xin Sui ◽

Bing Yu ◽

Youqing Shen ◽

Hailin Cong

Keyword(s):

Drug Resistance ◽

Clinical Practice ◽

Side Effects ◽

Drug Design ◽

Rational Drug Design ◽

Health Conditions ◽

Pharmacokinetic Parameters ◽

Single Target ◽

Rational Drug ◽

Huge Challenge

Multi-target drugs have gained considerable attention in the last decade owing to their advantages in the treatment of complex diseases and health conditions linked to drug resistance. Single-target drugs, although highly selective, may not necessarily have better efficacy or fewer side effects. Therefore, more attention is being paid to developing drugs that work on multiple targets at the same time, but developing such drugs is a huge challenge for medicinal chemists. Each target must have sufficient activity and have sufficiently characterized pharmacokinetic parameters. Multi-target drugs, which have long been known and effectively used in clinical practice, are briefly discussed in the present article. In addition, in this review, we will discuss the possible applications of multi-target ligands to guide the repositioning of prospective drugs.

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