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Overall, several obstacles exist in the in silico design of amino acid-based medicines
that need be addressed in order to improve the overall performance of the design pro-
cesses. For example, while determining the structure of all disease-related proteins
using crystallography and NMR is a time-consuming task, however, it should be done as
the significant structural information about peptide-protein interactions is indispensable.
Furthermore, the development of robust algorithms to compute protein–protein binding
energies is critical. Millisecond-scale moleculardynamicsisrequiredforthecalculation
of the binding constant between two macromolecules with a suitable speed-accuracy
tradeoff. Furthermore, understanding of protein–protein and protein–peptidomimetics
recognition processes at the molecular level can be improved by using more precise force
fields such as quantum mechanical polarizable force.
References
[1] Kohei, U., Atsushi, Y., Hengwei, C., Hiroyuki, K., Hiroyuki, N., & Jürgen, B. DeepCubist: Molecular
generator for designing peptidomimetics based on complex three-dimensional scaffolds. Journal of
Computer-Aided Molecular Design, 2023, 37, 107–115.
[2] Wanner, J., Fry, D. C., Peng, Z., & Roberts, J. Druggability assessment of protein-protein interfaces.
Future Medicinal Chemistry, 2011, 3, 2021–2038.
[3] D’Annessa, I., Di Leva, F. S., La Teana, A., Novellino, E., Limongelli, V., & Di Marino, D. Bioinformatics
and biosimulations as toolbox for peptides and peptidomimetics design: Where are we? Frontiers in
Molecular Biosciences, 2020, 7, 66.
[4] Di Marino, D., Achsel, T., Lacoux, C., Falconi, M., & Bagni, C. Molecular dynamics simulations show
how the FMRP Ile304Asn mutation destabilizes the KH2 domain structure and affects its function.
Journal of Biomolecular Structure and Dynamics, 2014, 32, 337 –350.
[5] Di Marino, D., Chillemi, G., De Rubeis, S., Tramontano, A., Achsel, T., & Bagni, C. MD and docking
studies reveal that the functional switch of CYFIP1 is mediated by a butterfly-like motion. Journal of
Chemical Theory and Computation, 2015, 11, 3401–3410.
[6] Orozco, M. A theoretical view of protein dynamics. Chemical Society Reviews, 2014, 43, 5051–5066.
[7] D’Annessa, I., Gandaglia, A., Brivio, E., Stefanelli, G., Frasca, A., Landsberger, N., et al. Tyr120Asp
mutation alters domain flexibility and dynamics of MeCP2 DNA binding domain leading to impaired
DNA interaction: Atomistic characterization of a Rett syndrome causing mutation. Biochimica Et
Biophysica Acta (Bba)-general Subjects, 2018, 1862, 1180–1189.
[8] D’Annessa, I., Cicconardi, F., & Di Marino, D. Handling FMRP and its molecular partners: Structural
insights into Fragile X Syndrome. Progress in Biophysics and Molecular Biology, 2019, 141, 3–14.
[9] Sliwoski, G., Kothiwale, S., Meiler, J., & Lowe, E. W. Computational methods in drug discovery.
Pharmacological Reviews, 2014, 66, 334–395.
[10] D’Annessa, I., Raniolo, S., Limongelli, V., DiMarino, D., & Colombo, G. Ligand binding, unbinding,
and allosteric effects: Deciphering small-molecule modulation of HSP90. Journal of Chemical Theory
and Computation, 2019, 15(11), 6368–6381.
[11] Di Marino, D., D’Annessa, I., Tancredi, H., Bagni, C., & Gallicchio, E. A unique binding mode of the
eukaryotic translation initiation factor 4E for guiding the design of novel peptide inhibitors. Protein
Science, 2015, 24, 1370–1382.
374 Kannan Sadasivam, Venkata Surya Kumar Choutipalli, and Lalitha Gummidi
https://t.me/med1917
[12] Vercelli, A., Biggi, S., Sclip, A., Repetto, I. E., Cimini, S., Falleroni, F., et al. Exploring the role of MKK7
in excitotoxicity and cerebral ischemia: A novel pharmacological strategy against brain injury. Cell
Death & Disease, 2015, 6, e1854.
[13] Wanner, J., Fry, D. C., Peng, Z., & Roberts, J. Druggability assessment of protein-protein interfaces.
Future Medicinal Chemistry, 2011, 3, 2021–2038.
[14] Otvos, L., & Wade, J. D. Current challenges in peptide-based drug discovery. Frontiers in Chemistry,
2014, 2, 62.
[15] Banting, F. G., Best, C. H., Collip, J. B., Campbell, W. R., & Fletcher, A. A. Pancreatic extracts in the
treatment of diabetes mellitus. Canadian Medical Association Journal, 1922, 12(3), 141–146.
[16] Elkinton, J. R., Hunt, A. D., Jr, et al. Effects of pituitary adrenocorticotropic hormone (ACTH) therapy.
Journal of the American Medical Association, 1949, 141(18), 1273–1279.
[17] Meerovitch, K., Torkildsen, G., Lonsdale, J., et al. Safety and efficacy of MIM-D3 ophthalmic solutions
in a randomized, placebo-controlled Phase 2 clinical trial in patients with dry eye. Clinical
Ophthalmology, 2013, 7, 1275–1285.
[18] Birk, A. V., Liu, S., Soong, Y., et al. The mitochondrial-targeted compound SS-31 re-energizes
ischemic mitochondria by interacting with cardiolipin. Journal of the American Society of
Nephrology, 2013, 24(8), 1250–1261.
[19] Lydia, A., Tejas, N., & Greg, M. T. Hitting undruggable targets: Viewing stabilized peptide
development through the lens of quantitative systems pharmacology. Trends in Biochemical
Sciences, 2019, 44, 241–257.
[20] Heinrich, R., Neel, B. G., & Rapoport, T. A. Mathematical models of protein kinase signal
transduction. Molecular Cell, 2002, 9, 957–970.
[21] MacKeigan, J. P., Murphy, L. O., & Blenis, J. Sensitized RNAi screen of human kinases and
phosphatases identifies new regulators of apoptosis and chemoresistance. Nature Cell Biology,
2005, 7, 591–600.
[22] Julien, S. G., Dube, N., Hardy, S., & Tremblay, M. L. Inside the human cancer tyrosine phosphatome.
Nature Reviews Cancer, 2011, 11, 35–49.
[23] Fischer, G., Rossmann, M., & Hyvönen, M. Alternative modulation of protein-protein interactions by
small molecules. Current Opinion in Biotechnology, 2015, 35, 78–85.
[24] London, N., Raveh, B., & Schueler-Furman, O. Druggable protein-protein interactions-from hot spots
to hot segments. Current Opinion in Chemical Biology, 2013, 17(6), 952–959.
[25] Bauer, R. A., Wurst, J. M., & Tan, D. S. Expanding the range of ‘druggable’ targets with natural
product-based libraries: An academic perspective. Current Opinion in Chemical Biology, 2010, 14(3),
308–314.
[26] Rezaei, A. R., & Keating, A. E. Designing helical peptide inhibitors of protein-protein interactions.
Current Opinion in Structural Biology, 2016, 39, 27–38.
[27] Merrifield, R. B. Solid phase peptide synthesis. I. The synthesis of a tetrapeptide. Journal of the
American Chemical Society, 1963, 85, 2149–2154.
[28] Gupta, V., et al. Protein PEGylation for cancer therapy: Bench to bedside. Journal of Cell
Communication and Signaling, 2019, 13, 319–330.
[29] Lau, J. L., & Dunn, M. K. Therapeutic peptides: Historical perspectives, current development trends,
and future directions. Bioorganic & Medicinal Chemistry, 2018, 26, 2700–2707.
[30] Chatterjee, J., Rechenmacher, F., & Kessler, H. N-methylation of peptides and proteins: An important
element for modulating biological functions. Angewandte Chemie International Edition, 2013, 52,
254–269.
[31] Smith, R. S., & Rudt, L. A. Ocular vascular and epithelial barriers to microperoxidase. Investigative
Ophthalmology, 1975, 14, 556–560.
14 Computer-aided design of peptidomimetic therapeutics 375
https://t.me/med1917
[32] Pitkänen, L., Ranta, V. P., Moilanen, H., & Urtti, A. Permeability of retinal pigment epithelium: Effects
of permeant molecular weight and lipophilicity. Investigative Ophthalmology & Visual Science, 2005,
46, 641–646.
[33] Schwarze, S. R., et al. In vivo protein transduction: Delivery of biologically active protein into the
mouse. Science, 1999, 285, 1569–1572.
[34] Vogel, B. E., et al. A novel integrin specificity exemplified by binding of the avb5 integrin to the basic
domain of the HIV Tat protein and vitronectin. Journal of Cell Biology, 1993, 121, 461–468.
[35] Carrasco, M. P., Jimenez-Lopez, J. M., Segovia, J. L., et al. Effects of ethanol on the remodeling of
neutral lipids and phospholipids in brain mitochondria and microsomes. Neurochemistry
International, 2007, 50, 858–865.
[36] Vagner, J., Qu, H., & Hruby, V. J. Peptidomimetics, a synthetic tool of drug discovery. Current Opinion
in Chemical Biology, 2008, 12, 292–296.
[37] Liskamp, R. M., Rijkers, D. T., Kruijtzer, J. A., & Kemmink, J. Peptides and proteins as a continuing
exciting source of inspiration for peptidomimetics. Chembiochem, 2011, 12, 1626–1653.
[38] Shaji, J., & Patole, V. Protein and Peptide drug delivery: Oral approaches. Indian Journal of
Pharmaceutical Sciences, 2008, 70, 269–277.
[39] Dhrisya, C., Sadhasivam, S., Sankar, G. R., Swaminathan, K., Musthafa, M. E., Feng-Huei, L., & Walid,
Q. M. Therapeutic efficacy of nanoparticles and routes of administration. Biomaterials Research,
2019, 23, 20.
[40] King, G. F. Venoms as a platform for human drugs: Translating toxins into therapeutics. Expert
Opinion Biological Therapy, 2011, 11, 1469–1484.
[41] Herzig, V., et al. Animal toxins – Nature’s evolutionary-refined toolkit for basic research and drug
discovery. Biochemical Pharmacology, 2020, 181, 114096.
[42] Coronado, M. A., et al. Structure of the polypeptide crotamine from the Brazilian rattlesnake
Crotalus durissus terrificus. Acta Crystallographica Section D: Biological Crystallography, 2013, 69,
1958–1964.
[43] Lee, I. H., Cho, Y., & Lehrer, R. I. Effects of pH and salinity on the antimicrobial properties of
clavanins. Infection and Immunity, 1997, 65(7), 2898– 2903.
[44] Ribosomally, P. M. Synthesized peptides with antimicrobial properties: Biosynthesis, structure,
function, and applications. Biotechnology Advances, 2003, 21(6), 465–499.
[45] Hodder, A. N., Christensen, J. J., Scally, S., Triglia, T., Ngo, A., Birkinshaw, R. W., Bailey, B., Favuzza, P.,
Dietrich, M. H., Tham, W. H., Czabotar, P. E., Lowes, K., Guo, K., Murgolo, N., Ruiz, M. L., McCauley,
J. A., Sleebs, B. E., Olsen, D. B., & Cowman, A. F. Basis for drug selectivity of plasmepsin IX and X
inhibition in Plasmodium falciparum and vivax. Structure, 2022, 30(7), 947–961.
[46] Favuzza, P., de Lera Ruiz, M., Thompson, J. K., Triglia, T., Ngo, A., Steel, R. W. J., Vavrek, M.,
Christensen, J., Healer, J., Boyce, C., Guo, Z., Hu, M., Khan, T., Murgolo, N., Zhao, L., Penington, J. S.,
Reaksudsan, K., Jarman, K., Dietrich, M. H., Richardson, L., Guo, K. Y., Lopaticki, S., Tham, W. H.,
Rottmann, M., Papenfuss, T., Robbins, J. A., Boddey, J. A., Sleebs, B. E., Sabroux, H. J., McCauley, J. A.,
Olsen, D. B., & Cowman, A. F. Dual plasmepsin-targeting antimalarial agents disrupt multiple stages
of the malaria parasite life cycle. Cell Host & Microbe, 2020, 27, 642–658.
[47] Shah, S. S., Casanova, N., Antuono, G., & Sabatino, D. Polyamide backbone modified cell targeting
and penetrating peptides in cancer detection and treatment. Frontiers in Chemistry, 2020, 8, 218.
[48] Jafari, S. H., Saadatpour, Z., Salmaninejad, A., Momeni, F., Mokhtari, M., Nahand, J. S., et al. Breast
cancer diagnosis: Imaging techniques and biochemical markers. Journal of Cellular Physiology,
2018, 233(7), 5200–5213.
[49] Kim, Y.-S., Zhou, Y., Bryant, H., Milenic, D. E., Baidoo, K. E., Lewis, B. K., et al. Synthesis and
characterization of gadolinium – Peptidomimetic complex as an α vβ3 integrin targeted MR contrast
agent. Bioorganic & Medicinal Chemistry Letters, 2015, 25(10), 2056–2059.
376 Kannan Sadasivam, Venkata Surya Kumar Choutipalli, and Lalitha Gummidi
https://t.me/med1917
[50] Zhou, P., Wang, C., Ren, Y., Yang, C., & Tian, F. Computational peptidology: A new and promising
approach to therapeutic peptide design. Current Medicinal Chemistry, 2013, 20, 1985–1996.
[51] Torrie, G. M., & Valleau, J. P. Nonphysical sampling distributions in Monte Carlo free-energy
estimation: Umbrella sampling. Journal of Computational Physics, 1977, 23, 187–199.
[52] Di Marino, D., D’Annessa, I., Tancredi, H., Bagni, C., & Gallicchio, E. A unique binding mode of the
eukaryotic translation initiation factor 4E for guiding the design of novel peptide inhibitors. Protein
Science, 2015, 24, 1370–1382.
[53] DiMarino, D., Achsel, T., Lacoux, C., Falconi, M., & Bagni, C. Molecular dynamics simulations show
how the FMRP Ile304Asn mutation destabilizes the KH2 domain structure and affects its function.
Journal of Biomolecular Structure and Dynamics, 2014, 32, 337 –350.
[54] Kilburg, D., & Gallicchio, E. Recent advances in computational models for the study of
protein–peptide interactions. Advances in Protein Chemistry and Structural Biology, 2016, 105,
27–57.
[55] Marlon, H. C., Raquel, Q. O., Samilla, B. R., Gisele, R., Karen, G. N. O., Elizabete, S. C., & Octávio,
L. F. Computer-aided design of antimicrobial peptides: Are we generating effective drug candidates?
Frontiers in Microbiology, 2020, 10, 3097.
[56] Porto, W. F., Silva, O. N., & Franco, O. L. Prediction and rational design of antimicrobial peptides. In:
Faraggi, E. (ed) Protein Structure. 2012, (pp. 377–396). InTech: London.
[57] Mitchell, J. B. Machine learning methods in chemoinformatics. Wiley Interdisciplinary Reviews:
Computational Molecular Science, 2014, 4, 468–481.
[58] Jenssen, H. Descriptors for antimicrobial peptides Expert Opinion Drug Discovery, 2011, 6, 171–184.
[59] Fjell, C. D., Hancock, R. E. W., & Jenssen, H. Computer-aided design of antimicrobial peptides.
Current Pharmaceutical Analysis, 2010, 6, 66–75.
[60] Hilpert, K., Fjell, C. D., & Cherkasov, A. Short linear cationic antimicrobial peptides: Screening,
optimizing, and prediction in peptide-based drug design. In: peptide-based drug design, Otvos,
L. (ed) 2008, (pp. 127 – 159). Springer): Berlin.
[61] Cruz-Monteagudo, M., Borges, F., & Cordeiro, M. N. Jointly handling potency and toxicity of
antimicrobial peptidomimetics by simple rules from desirability theory and chemoinformatics.
Journal of Chemical Information and Modeling, 2011, 51, 3060–3077.
[62] Wang, P., Hu, L., Liu, G., Jiang, N., Chen, X., Xu, J., Zheng, W., Li, L., Tan, M., Chen, Z., Song, H., Cai,
Y. D., & Chou, K. C. Prediction of antimicrobial peptides based on sequence alignment and feature
selection methods. PLoS One, 2011, 6, e18476.
[63] Chou, K. C., & Shen, H. B. Review: Recent advances in developing web-servers for predicting protein
attributes. Natural Sciences, 2009, 2, 63–92.
[64] Cruz-Monteagudo, M., Borges, F., Cordeiro, MNDS, Cagide Fajin, J. L., Morell, C., Molina Ruiz, R.,
Cañizares-Carmenate, Y., & Rosa Dominguez, E. Desirability-based methods of multiobjective
optimization and ranking for global qsar studies. Filtering safe and potent drug candidates from
combinatorial libraries. Journal of Combinatorial Chemistry, 2008, 10, 897–913.
[65] Nicolotti, O., Giangreco, I., Miscioscia, T. F., & Carotti, A. Improving quantitative structure-activity
relationships through multiobjective optimization. Journal of Chemical Information and Modeling,
2009, 49, 2290–2302.
[66] Di Leva, F. S., Tomassi, S., Di Maro, S., Reichart, F., Notni, J., Dangi, A., et al. From a helix to a small
cycle: Metadynamics-inspired avb6 integrin selective ligands. Angewandte Chemie International
Edition, 2018, 57, 14645–14649.
[67] Lama, D., Liberatore, A. M., Frosi, Y., Nakhle, J., Tsomaia, N., Bashir, T., et al. Structural insights
reveal a recognition feature for tailoring hydrocarbon stapled-peptides against the eukaryotic
translation initiation factor 4E protein. Chemical Science, 2019, 10, 2489–2500.
14 Computer-aided design of peptidomimetic therapeutics 377
https://t.me/med1917
[68] Lama, D., Quah, S. T., Verma, C. S., Lakshminarayanan, R., Beuerman, R. W., Lane, D. P., et al.
Rational optimization of conformational effects induced by hydrocarbon staples in peptides and
their binding interfaces. Scientific Reports, 2013, 3, 3451.
[69] D’Annessa, I., Saverio Di Leva, F., La Teana, A., Ettore, N., Vittorio, L., & Di Marino, D. Bioinformatics
and biosimulations as toolbox for peptides and peptidomimetics design: Where are we? Frontiers in
Molecular Biosciences, 2020, 7, 66.
[70] Rodbard, H. W. The clinical impact of GLP-1 receptor agonists in type 2 diabetes: Focus on the long-
acting analogs. Diabetes Technology & Therapeutics, 2018, 20, S233–S241.
[71] Yu, D., et al. Molecular mechanism of HIV-1 resistance to sifuvirtide, a clinical trial-approved
membrane fusion inhibitor. Journal of Biological Chemistry, 2018, 293, 12703–12718.
[72] Li, L., et al. Efficacy, stability, and biosafety of sifuvirtide gel as a microbicide candidate against HIV-
1. PLoS One, 2012, 7, e37381.
[73] Anthony, A., Pounder, R. E., Dhillon, A. P., & Wakefield, A. J. Similarities between ileal Crohn’s disease
and indomethacin experimental jejunal ulcers in the rat. Alimentary Pharmacology & Therapeutics,
2000, 14, 241–245.
378 Kannan Sadasivam, Venkata Surya Kumar Choutipalli, and Lalitha Gummidi
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Bhupender Nehra, Manoj Kumar
✶
, Pooja A. Chawla, Viney Chawla
✶
,
Monika, Honey Goel, and Imtiyaz Ahmed Najar
15 Developing safer therapeutic agents
through toxicity prediction
Abstract: Drug toxicity refers to the adverse effects or harmful reactions caused by a
drug when it is administered at normal therapeutic doses. These effects can arise in
various organs or systems in the body with their little to severe impact. The chemical
composition of the medicine, dosage, mode of administration, patient’smetabolism,
and other factors can all contribute to drug toxicity. In the early stages of the drug
development process, in silico techniques that make use of computational models and
data-driven methodologies are vital for evaluating possible toxicities related to novel
compounds. This chapter addresses different approaches and technologies for toxicity
prediction, emphasizing how they might shorten the time it takes to create new drugs
and reduce attrition in clinical trials related to safety. In-depth understanding of how
in silico toxicity prediction aids in the creation of safer and more potent therapeutic
medicines is therefore made possible for researchers, toxicologists, regulatory ex-
perts, and drug developers. It places a strong emphasis on integrating computational
techniques with conventional drug development procedures to improve decision-
making and lower development costs.
Keywords: Toxicity, In silico, Computational, Safer, Therapeutic
15.1 Introduction
Preclinical toxicology was primarily a descriptive field for many years, reporting
treatment-related side effects with great care and using them to estimate safety mar-
gins for potential new drugs. But in the recent years, technological advancements
✶
Corresponding author: Manoj Kumar, Department of Pharmaceutical Sciences, Guru Jambheshwar
University of Science and Technology, Hisar 125001, Haryana, India
✶
Corresponding author: Viney Chawla, University Institute of Pharmaceutical Sciences and Research,
Baba Farid University of Health Sciences, Faridkot 151203, Punjab, India,
email: drvineychawla@gmail.com
Bhupender Nehra, Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science
and Technology, Hisar 125001, Haryana, India
Pooja A. Chawla, Monika, Honey Goel, University Institute of Pharmaceutical Sciences and Research,
Baba Farid University of Health Sciences, Faridkot 151203, Punjab, India
Imtiyaz Ahmed Najar, Department of Pharmacology, Lovely Professional University, Jalandhar, Punjab,
India
https://doi.org/10.1515/9783111207117-015
https://t.me/med1917
have made it possible for researchers to learn more about the mechanisms underly-
ing toxicity, which has helped to advance our understanding of the relevance of spe-
cies to humans, the predictability of safety events, the reduction of adverse effects,
and the creation of safety biomarkers [1]. As a result, the field of investigative toxicol-
ogy, also known as mechanistic toxicology, has gained prominence and is now essen-
tial to the pharmaceutical industry. It is well known that drug ADMET qualities
should be taken into account as early as feasible to lower failure rates in the clinical
phase of drug discovery, as unfavorable pharmacokinetics and toxicity are major
causes of drug development failure in the expensive late stage [2]. Pharmaceutical
companies have been paying more attention to the safety assessment of preclinical
medications as a result of the rising frequency of drug recalls in recent years. Preclini-
cal applications today use more advanced in vitro and in vivo drug evaluation proce-
dures; however these technologies come at a high expense. As computer science has
advanced quickly in recent years, in silico technology has been frequently employed
to assess pertinent drug features during the preclinical stage and has resulted in the
creation of numerous software applications [3]. Drug development can be split down
into multiple stages such as disease-related genetics, identifying targets and valida-
tion, lead identification and optimization, preclinical investigations, and clinical trials
as shown in Figure 15.1. It is a complex, hazardous, and time-consuming process. Phar-
macokinetics and toxicities are typically studied at a later point in the early stages of
drug discovery, but the activities and specificities of candidate medications are typi-
cally assessed early. Many potential medications failed at the end stage due to unfa-
vorable safety and efficacy, which were mostly brought on by ADMET characteristics
(absorption, distribution, metabolism, elimination, and toxicity). Based on a longitudi-
nal investigation, Cook et al. [4] conducted a thorough analysis of AstraZeneca’s small-
molecule therapeutic initiatives from 2005 to 2010. It was discovered that the primary
causes of more than 50 percent of the project’s closure failures were inadequate
safety and toxicity. In order to lower the ra te of turnover in drug development and
research, it has been acknowledged and extensively utilized that, similar to the evolu-
tion of drug discovery, it is critical to filter and optimize the ADMET features for med-
ications at an early stage. Preclinical drug pharmacokinetics and toxicity evaluations
are very helpful in lowering the number of new chemical entities that fail clinical tri-
als [4–6].
Although ADMET prediction techniques, both in vitro and in vivo, have gained
popularity recently, conducting costly and intricate ADMET tests on a large number of
chemicals is not feasible [7]. As a high-throughput, low-cost substitute for experimen-
tal measurement techniques, an in silico approach to forecast ADMET qualities has
grown in popularity. The capacity to synthesize compounds quickly, apply combinato-
rial chemistry, and screen compounds through high throughput have all significantly
expanded with the rapid growth of computer technologi es. Additionally, there has
been a noticeable increase in the initial requirements for ADMET data for lead drugs
(Figure 15.2), and the number of techniques for assessing ADMET in vitro is steadily
380 Bhupender Nehra et al.
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rising. In vivo models have been replaced by in silico simulation for the forecasting of
pharmacokinetics, toxic exposure, and other characteristics [8, 9].
Numerous in silico methods have been successfully utilized in addition to the
in vitro prediction of ADMET. With cheminformatics’ ongoing advancement, in silico
Figure 15.1: Systemic procedure of drug discovery and development.
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ADMET prediction has advanced and entered the big data age [6]. For ADMET predic-
tion, two types of in silico approaches can be utilized: data modeling and molecular
modeling. Protein structures in three dimensions serve as the foundation for molecular
modeling. It involves a variety of techniques including quantum mechanics (QM) com-
putation, molecular docking, and molecular dynamics (MD) simulation [10]. Physio-
logically based pharmacokinetic (PBPK) modeling and quantitative structure–activity
relationship (QSAR) are two examples of data modeling. A number of ADMET software
programs able to perform thorough estimation of property have been developed in re-
sponse to the increasing array of properties required to be predicted [11]. A complex
procedure of anticipating property variables from fewer to higher ones at early to final
timepoints has been involved in the development of ADMET software from in silico
techniques. In this chapter, we provide a summary of the popular databases and
ADMET prediction software along with certain issues and difficulties that computer
model prediction techniques and tools must overcome for further research and devel-
opment in this field.
Figure 15.2: Some well-known types of ADMET parameters.
382 Bhupender Nehra et al.
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15.2 In silico methods
15.2.1 Molecular modeling technique
Molecular modeling, which incorporates techniques like pharmacophore modeling, mo-
lecular docking, MD simulations, and QM calculations, is a crucial category in the predic-
tion of ADMET properties [12]. Also, toxicity profile assessment has somehow multistage
process as shown in Figure 15.3. It is based on the three-dimensional configurations of
proteins. Molecular modeling can supplement or even surpass QSAR studies as more
and more 3D models of ADMET proteins get accessible. The adaptable and substantial
binding cavities of ADMET proteins pose a challenge when performing ADMET predic-
tion through molecular modeling. Numerous encouraging outcomes of molecular model-
ing for compound metabolism prediction have been documented. In these situations, the
techniques fall into two categories: ligand-based and structure-based analyses. They aid
in the analysis of metabolic characteristics as well as the further optimization of com-
pound toxic effects, bioavailability, and other factors too [12, 13].
Figure 15.3: Steps to generate molecular models for toxicity assessment.
15 Developing safer therapeutic agents through toxicity prediction 383
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