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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5606_Библиотеки_им_академика_М_И_Перельмана.pdf
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- •Foreword
- •Acknowledgments
- •Contents
- •1.1 Structure-Based Drug Discovery (SBDD)
- •1.2 Ligand-Based Drug Design (LBDD)
- •1.3 Echoes from the Past, Visions from the Future
- •References
- •1 Introduction
- •2.2 Second Step: Data Curation
- •2.4 Fourth Step: Updating and Maintenance
- •2 Databases and Curation
- •8 Perspectives
- •9 Conclusion
- •References
- •1 Introduction
- •2.1 Making and Matching Protein Models
- •2.2 Simulating Protein Movements
- •2.3 Analyzing Changes in Protein Shape
- •3 Pharmacogenomics in Drug Development
- •4 Case Studies of Genomics-Based Drug Design
- •References
- •1 Historical Background
- •1.1 Timeline
- •2 Methodology Overview
- •2.1 Neural Networks
- •2.1.1 Perceptron
- •2.1.2 Multilayer Neural Networks
- •2.1.3 Types of Neural Networks
- •Feedforward
- •Recurrent Neural Networks
- •LSTM
- •2.2 Deep Learning
- •3 Using Machine Learning
- •3.2 Data Collection
- •3.3 Data Preprocessing
- •3.4 Model Selection
- •3.5 Model Training
- •3.6 Validation
- •3.7 Tuning
- •3.8 Prediction
- •4 Limitations
- •4.1 Bias
- •4.3 Interpretability
- •4.4 Computational Cost
- •4.5 Data Dependency
- •4.6 Robustness
- •5 Applications in Drug Discovery
- •5.2 Lead Discovery
- •5.3 Preclinical and Clinical Development
- •6 Resources and Tools
- •7 Challenges and Perspectives
- •7.1 Future Trends
- •9 Conclusions
- •References
- •1 Historical Background
- •1.1 Applications in Drug Discovery
- •2 Validations and Controls
- •2.1 Internal Validation
- •2.2 External Validation
- •2.3 Relative Cluster Validation
- •3 Challenges and Perspectives
- •4 Conclusions
- •References
- •1 Historical Background
- •2 OECD Principles
- •2.1 A Defined Endpoint
- •2.2 An Unambiguous Algorithm
- •2.5 A Mechanistic Interpretation, if Possible
- •3 Software and Tools
- •4 Validations and Controls
- •4.1 Internal and External Validation
- •4.1.1 Regression Metrics
- •4.2 Applicability Domain
- •4.3 Randomization Tests
- •5 Interpretation
- •6 Practical Advice During QSAR Modeling
- •7 Application
- •8 Challenges and Perspectives
- •References
- •1 Molecular Docking
- •2 Advances in Scoring Functions and Search Algorithms
- •2.2 Critical Characteristics of Search Algorithms
- •2.3 Docking Programs and Scoring Functions
- •3 Calculations Performed During Docking Simulations
- •4 Essential Components for a Good Docking Program
- •5 Limitations of the Docking Technique
- •6 Validation of Docking Results
- •7 Inappropriate Use of Validation Methods in Docking
- •9 Use of Machine Learning in Molecular Docking
- •11 Challenges
- •12 Conclusions
- •References
- •3 System Preparation for MD Simulations
- •3.1 Solvation and Microensemble
- •3.2 Force Fields: General Concept and Relevant Choices
- •3.3 The Concept of Replicas and Timescale
- •4.1.2 Protein Root Mean Square Fluctuation (RMSF)
- •4.1.4 Protein Secondary Structure Analysis
- •4.1.5 Principal component Analysis (PCA)
- •4.1.6 Markov State Modelling
- •4.1.7 Distance Calculations
- •4.1.8 Angle and Plane Calculations
- •4.2.2 Distances and Ligand-Induced Geometry Rearrangements
- •4 Molecular Dynamics Analysis
- •4.1 Protein Perspective
- •4.1.1 Protein Root Mean Square Deviation (RMSD)
- •4.3 Ligand Perspective
- •4.3.1 Ligand Properties
- •4.3.2 Ligand Root Mean Square Deviation
- •4.3.3 Ligand Root Mean Square Fluctuation
- •4.3.4 Angles and Dihedrals
- •5.1 Protein Structure Prediction and Preparation
- •5.2 Molecular Docking
- •6 Concluding Remarks and Outlook
- •Glossary
- •References
- •1 Introduction
- •2.1 MDeNM
- •2.2 Collective Molecular Dynamics (coMD)
- •2.3 ClustENM and ClustENMD
- •3 Ensemble Docking
- •References
- •1 Introduction
- •1.1 Advantages, Disadvantages, Innovations, and Challenges
- •1.2 Recent Advances in Accessible FEP Software Tools
- •1.3 Applications of FEP in Industry and Consortiums
- •2 Expanding the Potential of FEP Calculations
- •2.1 Validating Binding Poses
- •2.2 Dealing with Solvent
- •2.3 FEP and Allostery
- •2.4 FEP and Covalent Ligands
- •2.5 Applications of FEP in Scaffold Hopping
- •2.6 Positional Analogue Scanning
- •2.7 Combinations and Alternative Approaches
- •3 Machine Learning for FEP
- •3.4 Implications for ML in FEP Calculations
- •4 Final Considerations
- •5 First Steps to FEP Simulations
- •References
- •1 Background
- •2 Ultra-Large Screening Libraries and Chemical Spaces
- •3.1 Implications of Dataset Size
- •4 Ligands on the Ultra-Large Scale
- •4.1 Ultra-Large 2D Similarity Searches
- •7 Challenges and Future Perspectives
- •7.1 Hit Triage: An Old Problem on a New Dimension
- •8 Conclusions
- •Appendix
- •References
- •1 Introduction
- •2 Enzymatic Activity Evaluations
- •3 Cytotoxicity Evaluation and Cell Viability
- •4 Antiviral Assays in Experimental Validation
- •6 In Vivo Evaluation of Compounds
- •7 Conclusions
- •References
- •1 Introduction
- •3.1 Data Collection
- •3.2 Data Preprocessing
- •3.4 Model Choice
- •3.5 Model Training
- •3.6 Model Assessment
- •3.7 External Validation
- •3.8 Implementation and Availability
- •3.9 Continuous Update
- •5 Conclusions and Perspectives
- •References
- •1 Experimental Approaches to Obtain Protein Structure
- •1.1 X-Ray Crystallography
- •1.2 Nuclear Magnetic Resonance
- •1.3 Cryo-EM
- •1.4 Hybrid Methods
- •2 Modeling Approaches to Obtain Protein Structure
- •2.1 Homology Modeling
- •2.2 Ab Initio Modeling
- •2.3 New Approaches
- •3 Conformational Diversity of Proteins
- •3.1 Characterization of Protein Conformational States
- •3.2 Experimental Methods to Study Protein Dynamics and Conformations
- •3.4 Molecular Dynamics Simulation
- •3.5 Sampling Strategies
- •4 Remarks and Perspectives
- •References
- •1 Introduction
- •2 Structure-Based Drug Design of HIV Protease Inhibitors
- •2.1 HIV-1 Protease as a Therapeutic Target
- •2.2.1 Saquinavir
- •2.2.2 Indinavir
- •2.3.1 Lopinavir
- •2.3.2 Darunavir
- •6 Conclusions
- •References
- •4 Experimental Methods to Analyze NR Activity
- •4.2 Coregulator-Recruitment
- •5 Concluding Remarks and Outlook
- •References

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Chapter 13
Challenges Faced in the Development
of Computational Methods for Predicting
Pharmacokinetics Behavior
José Eduardo Gonçal ves
Abstract The pharmacokinetic behavior of a drug is determined by the intricate
interplay between the physicochemical properties of the molecule and its multifaceted interactions with the biological system from the moment of administration until
its elimination from the body. Consequently, the determination and evaluation of
pharmacokinetic events constitute a complex process, which inevitably results in an
equal or greater complexity in predictive studies. In light of this, it becomes evident
that predictive methods employing computational models will present challenges
that must be addressed to enable their broad development and application. To this
end, it is essential to acquire detailed knowledge of the stages involved in the model
creation process and identify the critical points requiring attention to minimize
potential failures or low predictive power of computational methods. In this chapter,
this approach will be employed, bringing forth experiences available in the literature
on the process of creating computational models for predicting pharmacokinetic
behavior at the early stages of drug development, highlighting the main challenges
commonly encountered, and the strategies typically employed to mitigate prediction
issues faced by different research groups in this field.
Keywords In silico PK models · ADME prediction · Nonclinical studies
1 Introduction
The development of a new drug consists of a process in which various pieces of
information regarding the efficacy and safety of the molecule in the biological
system must be obtained. This mainly includes those related to the effect when
acting on a specific target (pharmacodynamics), those related to how the molecule is
disposed within the organism (pharmacokinetics), and the potential undesirable
J. E. Gonçalves (✉)
Produtos Farmacêuticos, Faculdade de Farmácia da Universidade Federal de Minas Gerais, Belo
Horizonte, Minas Gerais, Brazil
e-mail: jegoncalves@ufmg.br
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024
V. G. Maltarollo (ed.), Computer-Aided and Machine Learning-Driven Drug
Design, Computer-Aided Drug Discovery and Design 3,
https://doi.org/10.1007/978-3-031-76718-0_13
385

386 J. E. Gonçalves
effects of this xenobiotic (toxicology). Such information is acquired in various
successive stages, starting from the initial phases of development, the so-called
nonclinical studies, and the clinical phase, which involves evaluation in human
subjects [1]. Obtaining early insights into the efficacy, potential toxicity, and pharmacokinetics behavior of candidate drug molecules has been observed to reduce
failures in subsequent stages [2].
Pharmacokinetic behavior is the result of multiple interactions between the drug
and the biological system, influenced by multifactorial elements that drive the
processes of absorption, distribution, metabolism, and excretion [1, 2]. In view of
this, one must bear in mind the complexity involved in evaluating and determining
the pharmacokinetics characteristics that lead to the disposition of the drug in
the body.
In an effort to generate knowledge about the pharmacokinetic behavior of a drug
from the early stages of development, several in vitro and in vivo studies are
conducted in nonclinical stage, as apparent permeability studies, animal absolute
oral bioavailability, tissue drug distribution, plasma protein binding, enzymatic
metabolism, blood-brain barrier permeability, among other methods.
All these studies are subject to limitations and pose a considerable challenge in
the development of new drugs. A critical obstacle involves the costs associated with
conducting extensive studies on numerous promising molecules. Only after rigorous
evaluation of pharmacokinetic data, desired therapeutic effects, and toxicological
profiles can a compound be considered for clinical trials. In this context, in silico
methods emerge as an important complementary tool. These methods employ
computational models and artificial intelligence to predict pharmacokinetics characteristics in the early stages of development. The objective of in silico methods is to
facilitate the evaluation of a larger number of candidates through high-throughput
screening. This approach minimizes the probability of failures by discarding
unpromising molecules and guiding potential molecular modifications to enhance
desirable pharmacokinetic characteristics [4].
Several generations of in silico models have emerged as a tool in research and
development, many of which are widely available through open-access platforms.
The success of using computational methods for pharmacokinetic prediction has
been demonstrated over the years. A study published in 2014 shows the effectiveness of reducing failures in the development of new drugs by employing in silico
methods alongside other tools in early-stage evaluations. This study observed a
gradual and significant decrease in clinical study discontinuation rates, from around
40% in the 1990s to approximately 1% by 2007 [5]. These findings suggest the
positive impact that computational methods have on the pharmaceutical industry.
However, it is crucial to emphasize that in silico predictions neither replace nor
disqualify experimental testing. On the contrary, their goal is to complement experimental results and, together with other relevant scientific evidence, provide support
for decision-making [5]. Nevertheless, there are several challenges in modeling
pharmacokinetic properties using computational methods, and this chapter will
discuss the key aspects of this topic.

13 Challenges Faced in the Development of Computational Methods... 387
2 Main Physicochemical and Pharmacokinetic
Characteristics Used in Developing Computational
Methods
As previously mentioned, the bioavailab ility encompassing absorption, distribution,
metabolism, and elimination of a drug, which constitute pharmacokinetic processes,
result from the interaction between the drug molecule and various biological structures within the body. These interactions are influenced even prior to their occurrence by the biopharmaceutical properties of the drug and the formulation through
which it is administered. The factors influencing this pharmacokinetics behavior,
which are used to develop predictive computational models, can generally be
divided into three levels. These levels reflect the complexity and detail each factor
contributes to the model [ 6 ].
At Level 1, fundamental biopharmaceutical properties, including physicochemical characteristics, are assessed. These properties encompass solubility, partition
coefficient, hydrogen bond donors and acceptors, and adherence to established
guidelines such as Lipinski ’ s Rule of Five and Veber’s criteria [2].
Level 2 incorporates information related to pharmacokinetic characteri stics. This
level encompasses parameters indicative of absorption, distribution, metabolism,
and excretion processes.
At Level 3, models based on physiological, biochemical, and anatomical data are
utilized, allowing for the prediction of how a drug will behave under different
biological conditions. These methods are identified as physiologically based pharmacokinetic methods (PBPK).
Table 13.1 presents the main components commonly employed in the establishment of computational models. These components are classified into three levels,
along with examples of developed platforms available for predicting these
pharmacokinetic-related characteristics.
3 Construction of an In Silico Model to Predict ADME
Properties
The construction of a model, also known as the computational modeling process, for
predicting pharmacokinetics, involves several steps. These range from the collection
of data used to feed the model to the validation of the model itself. These steps are
outlined below.
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