Preface
Page: i-i (1)
Author: Dilpreet Singh and Prashant Tiwari
DOI: 10.2174/9789815223019124010001
Introduction to Computer-Based Simulations and Methodologies in Pharmaceutical Research
Page: 1-24 (24)
Author: Samaresh Pal Roy*
DOI: 10.2174/9789815223019124010003
PDF Price: $15
Abstract
Pharmaceutical research is increasingly using computer-based simulations
and approaches to hasten the identification and development of new drugs. These
methods make use of computational tools and models to forecast molecular behavior,
evaluate therapeutic efficacy, and improve drug design. Molecular modeling is a key
application of computer-based simulations in pharmaceutical research. It allows
researchers to build virtual models of molecules and simulate their behavior, which
provides insights into their interactions and properties. Molecular docking is a
computational method used in Computer-Aided Drug Design (CADD) to predict the
binding mode and affinity of a small molecule ligand to a target protein receptor.
Quantitative structure-activity relationship (QSAR) modeling is another pharmaceutical
research tool. QSAR models predict molecular activity based on the chemical structure
and other attributes using statistical methods. This method prioritizes and optimizes
drug candidates for specific medicinal uses, speeding up drug discovery. Another
effective use of computer-based simulations in pharmaceutical research is virtual
screening. It entails lowering the time and expense associated with conventional
experimental screening methods by employing computational tools to screen huge
libraries of chemicals for prospective therapeutic candidates. While computer-based
techniques and simulations have many advantages for pharmaceutical research, they
also demand a lot of processing power and knowledge. Also, they are an addition to
conventional experimental procedures rather than their replacement. As a result, they
frequently work in tandem with experimental techniques to offer a more thorough
understanding of drug behavior and efficacy. Overall, computer-based simulations and
methodologies enable pharmaceutical researchers to gather and analyze data more
efficiently, bringing new medications and therapies to market.
Tools for the Calculation of Dissolution Experiments and their Predictive Properties
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Author: Ram Babu S.*, Sakshi T. and Amardeep K.
DOI: 10.2174/9789815223019124010004
PDF Price: $15
Abstract
Dissolution testing, which establishes the rate and extent of the drug release
from pharmaceutical products intended for oral administration, has been recognized as
a crucial method for drug development and quality control of dosage form. Dissolution
studies also help in establishing the in vitro and in vivo correlative studies, i.e., they can
predict drug release and absorption without performing the study inside living things.
The calculation and interpretation of dissolution data is a very typical task but it has
been made simple by using various software and mathematical tools that easily analyze
and illustrate the drug release data with their interpretation. Currently, most
pharmaceutical companies believe in real-time prediction of dissolution profiles, which
they have done due to their market position and increasing demand. Because of their
competitiveness and rising demand, the majority of pharmaceutical businesses now
support real-time prediction of dissolution profiles. As a result, alternative methods
have been added to acquire a rapid response, such as spectroscopic approaches,
particularly near-infrared spectroscopy (NIRS), which gathers the data based on the
physicochemical features of the dosage form. Advanced multivariate analytic
approaches, such as principal component analysis (PCA), principal component
regression, and classical least squares regression, are widely employed to extract such
data for use in quantitative modelling. There is still a dearth of research into the
combined impact of numerous critical factors and their interactions on dissolution,
despite several studies showing that drug product dissolution profiles can potentially be
predicted from material, formulation, and process information using advanced
mathematical approaches.
The Role of Principal Component Analysis in Pharmaceutical Research: Current Advances
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Author: Diksha Sharma, Anjali Sharma, Punam Gaba, Neelam Sharma*, Rahul Kumar Sharma and Shailesh Sharma
DOI: 10.2174/9789815223019124010005
PDF Price: $15
Abstract
Karl Pearson developed Principal Component Analysis (PCA) in 1901 as a
mathematical equivalent of the principal axis theorem. Later on, it was given different
names according to its application in various fields. Principal Component Analysis
provides a foundation for comprehending the fundamental workings of the system
under examination. It has various applications in different fields such as signal
processing, multivariate quality control, psychology, biology, meteorological science,
noise and vibration analysis (spectral decomposition), and structural dynamics. In this
chapter, we will discuss its application in pharmaceutical research and drug discovery.
This technique allows for the representation of multidimensional data and the
evaluation of large datasets to improve data interpretation while retaining the maximum
amount of information possible. PCA is a technique that does not require extensive
computations and offers reduced memory and storage requirements. PCA can be
conceptualized as an n-dimensional ellipsoid fitted to the data, with each axis
representing a principal component. The ellipse's axes are determined by subtracting
the mean of each variable from the datasheet. In the pharmaceutical research field,
original variables are often expressed in various measurement units. Therefore, the
original variables are divided by their standard deviation once the mean has been
subtracted. This step is taken to work with z-scores, which are further used for
extracting the eigenvalues and eigenvectors of the original data.
Quality by Design in Pharmaceutical Development: Current Advances and Future Prospects
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Author: Popat Mohite*, Amol Gholap, Sagar Pardeshi, Abhijeet Puri and Tanavirsing Rajput
DOI: 10.2174/97898152230191240100006
PDF Price: $15
Abstract
QbD, or Quality by Design, is a cutting-edge methodology adopted
extensively in the pharmaceutical industry. It is defined objects, such as the product's
safety and effectiveness. QbD's primary focus in the pharmaceutical industry is
ensuring the product's security and usefulness. Quality by Design (QbD) seeks to instill
high standards of excellence in the blueprinting process. The International Council for
Harmonization (ICH) has developed guidelines and elements that must be adhered to
guarantee the consistent, high-quality development of pharmaceuticals. This chapter
provides updated guidelines and elements, including quality risk management,
pharmaceutical quality systems, QbD in analytical methods and pharmaceutical
manufacturing, process control, vaccine development, pharmacogenomic, green
synthesis, etc. QbD was briefly defined, and several design tools, regulatory-industry
perspectives, and QbD grounded on science were discussed. It was portrayed that
significant effort was put into developing drug ingredients, excipients, and
manufacturing processes. Quality by design (QbD) is included in the manufacturing
process's development, and the result is steadily improving product quality. Quality
target product profiles, critical quality attributes, analytical process techniques, critical
process parameters control strategy and design space are elements of many
pharmaceutical advancements. Some of the topics covered included the application of
QbD to herbal products, food processing, and biotherapeutics through analytical
process techniques. We are still exploring and compiling all the data and metrics
required to link and show the benefits of QbD to all stakeholders. Nevertheless, the
pharmaceutical sector is quickly using the QbD process to create products that are
reliable, efficient, and of high quality. Soon, a more profound comprehension of the
dosage form parameters supported by the notion of QbD will benefit Risk management
and process and product design, optimizing complex drug delivery systems.
Virtual Tools and Screening Designs for Drug Discovery and New Drug Development
Page: 108-134 (27)
Author: Sonal Dubey*
DOI: 10.2174/9789815223019124010007
PDF Price: $15
Abstract
The synergy between virtual tools and screening designs has catalyzed a
transformative shift in drug discovery and new drug development. Leveraging
computational models, molecular simulations, and artificial intelligence, virtual tools
empower researchers to predict molecular interactions, assess binding affinities, and
optimize drug-target interactions. This predictive capacity expedites the identification
and prioritization of promising drug candidates for further investigation.
Simultaneously, screening designs facilitate systematic and high-throughput evaluation
of vast compound libraries against target proteins, enabling the rapid identification of
lead compounds with desired pharmacological activities. Advanced data analysis
techniques, including machine learning, enhance the efficiency and accuracy of hit
identification and optimization processes. The integration of virtual tools and screening
designs presents a holistic approach that accelerates the drug discovery pipeline. By
expounding on rational drug design, these tools guide the development of novel
compounds with enhanced properties. Furthermore, this approach optimizes resource
allocation by spotlighting high-potential candidates and minimizing costly
experimental iterations. As an outcome of this convergence, drug discovery processes
are becoming more precise, efficient, and cost-effective. The resulting drug candidates
exhibit improved efficacy, specificity, and safety profiles. Thus, the amalgamation of
virtual tools and screening designs serves as a potent catalyst for innovation in drug
discovery and new drug development, ensuring the delivery of transformative therapies
to address unmet medical challenges. In this chapter, we shall be discussing different
tools in detail with actual examples leading to successful stories.
Predicting Drug Properties: Computational Strategies for Solubility and Permeability Rates
Page: 135-152 (18)
Author: Anshita Gupta Soni*, Renjil Joshi, Deependra Soni, Chanchal Deep Kaur, Swarnlata Saraf and Pankaj Kumar Singh
DOI: 10.2174/9789815223019124010008
PDF Price: $15
Abstract
The oral bioavailability of a medicine can be considerably influenced by its
water solubility, which can also have an impact on how the drug is dispersed through
the body. To decrease the likelihood of failures in the late phases of drug development,
aqueous solubility must be taken into account early in the drug research and
development process. By using computer models to predict solubility, combinatorial
libraries might be screened to identify potentially problematic chemicals and exclude
those with insufficient solubility. In addition to predicting solubility from chemical
structure, the explanation of such models can provide insight into correlations between
structure and solubility and can direct structural improvement to improve solubility
while preserving the effectiveness of the medications under study. Such model
development is a difficult procedure that calls for taking into account a wide range of
variables that may affect how well the model performs in the end. In this article,
various solubility modeling techniques are presented. Despite many studies on model
creation, predicting the solubility of various medications remains difficult. One of the
primary reasons for the poor trustworthiness of many of the suggested models is the
quality of the experimental data that may be used to simulate solubility, which is
becoming more widely acknowledged. Consequently, increased availability of
trustworthy data produced using the same experimental technique is necessary to fully
realize the potential of the established modeling tools.
Pharmacokinetic and Pharmacodynamic Modeling (PK/PD) in Pharmaceutical Research: Current Research and Advances
Page: 153-169 (17)
Author: Richa Sood* and Anita A.
DOI: 10.2174/9789815223019124010009
PDF Price: $15
Abstract
The development of more intricately constructed molecules and drug
delivery systems as a result of technological breakthroughs has increased our
understanding of the complexities of disease and allowed us to identify a wide range of
therapeutic targets. New drug combinations can be designed by correctly using
dynamical systems-based PK/PD models. The unswerving approach that offers a better
knowledge and understanding of therapeutic efficacy and safety is the use of
pharmacokinetic-pharmacodynamic (PK-PD) modeling in drug research. In vivo,
animal testing or in vitro bioassay is used to forecast efficacy and safety in people.
Model-based simulation using primary pharmacodynamic models for direct and
indirect responses is used to elucidate the assumption of a fictitious minimal effective
concentration or threshold in the exposure-response relationship of many medicines. In
this current review, we have abridged the basic PK-PD modeling concepts of drug
delivery and documented how they can be used in current research and development.
Experimental Tools as an “Alternative to Animal Research” in Pharmacology
Page: 170-206 (37)
Author: Kunjbihari Sulakhiya*, Rishi Paliwal, Anglina Kisku, Madhavi Sahu, Shivam Aditya, Pranay Soni and Saurabh Maru
DOI: 10.2174/9789815223019124010010
PDF Price: $15
Abstract
Experimental tools have emerged as a promising alternative to animal
research in pharmacology. With growing ethical concerns and regulatory restrictions
surrounding animal experimentation, researchers are increasingly turning towards in
vitro and in silico methods to develop new drugs and evaluate their safety and efficacy.
In vitro tools include cell culture systems, 3D organoid models, and microfluidic
devices replicating complex physiological conditions, such as the blood-brain barrier or
the liver microenvironment. These systems can provide more accurate and predictive
results than animal models, reducing ethical concerns and experimental costs. In silico
methods, such as computer modelling, simulation, and artificial intelligence, enable
researchers to predict the drug-target interactions, toxicity, and pharmacokinetic and
pharmacodynamic properties of new drugs without animal testing. Experimental tools
have several advantages over animal research, including more accurate and predictive
results, lower costs, higher throughput, and reduced ethical concerns. However, the
limitations of these tools must also be acknowledged, such as the inability to fully
replicate the complexity of a living organism, which requires further validation. These
tools offer a promising avenue for advancing pharmacological research while reducing
the reliance on animal experimentation. In conclusion, experimental tools provide an
excellent alternative to animal research in pharmacology to identify and avoid potential
toxicities early in the drug discovery process and have the potential to revolutionize
drug discovery and development. This chapter mainly focuses on the numerous in
vitro, in silico, non-animal in vivo, and emerging experimental tools and their
regulatory perspectives on validation, acceptance, and implementation of the
alternative methods used in pharmacological research.
Newer Screening Software for Computer Aided Herbal Drug Interactions and its Development
Page: 207-226 (20)
Author: Sunil Kumar Kadiri* and Prashant Tiwari
DOI: 10.2174/9789815223019124010011
PDF Price: $15
Abstract
Self-diagnosis and treatment by consumers as a means of reducing medical
costs contribute to the predicted continued growth in the usage of herbal products.
Herbal products are notoriously difficult to evaluate for potential drug interactions
because of the wide range of possible interactions, the lack of clarity surrounding the
active components, and the often insufficient knowledge of the pharmacokinetics of the
offending constituents. It is a standard practice for innovative drugs in development to
identify particular components from herbal goods and describe their interaction
potential as part of a systematic study of herbal product drug interaction risk. By
cutting down on expenses and development times, computer-assisted drug design has
helped speed up the drug discovery process. The natural origins and variety of
traditional medicinal herbs make them an attractive area of study as a complement to
modern pharmaceuticals. To better understand the pharmacological foundation of the
actions of traditional medicinal plants, researchers have increasingly turned to in silico
approaches, including virtual screening and network analysis. The combination of
virtual screening and network pharmacology can reduce costs and improve efficiency
in the identification of innovative drugs by increasing the proportion of active
compounds among candidates and by providing an appropriate demonstration of the
mechanism of action of medicinal plants. In this chapter, we propose a thorough
technical route that utilizes several in silico approaches to discover the pharmacological
foundation of the effects of medicinal plants. This involves discussing the software
used in the prediction of herb-drug interaction with a suitable database.
Deliberations and Considerations of Mesodyn Simulations in Pharmaceuticals
Page: 227-248 (22)
Author: Manisha Yadav, Dhriti Mahajan, Om Silakari and Bharti Sapra*
DOI: 10.2174/9789815223019124010012
PDF Price: $15
Abstract
The main aim of this chapter is the detailed analysis of the Mesodyn module
and how it is beneficial in the pharmaceuticals or drug delivery systems. These models
are the generalization of a coarse-grained model in mesoscopic dynamics which is used
for the field-based simulations of complex systems. A set of functional Langevin
equations characterize the system’s behavior. These computer-based simulation tools
have been proven effective for providing information at molecular and mesoscopic
scales and also for overcoming the limitations of wet lab experiments. So, this chapter
will discuss the potential use of Mesodyn simulations in pre-formulations and various
other applications for the rational designing of drug delivery systems after providing a
brief theoretical background.
Computational Tools to Predict Drug Release Kinetics in Solid Oral Dosage Forms
Page: 249-279 (31)
Author: Devendra S. Shirode*, Vaibhav R. Vaidya and Shilpa P. Chaudhari
DOI: 10.2174/9789815223019124010013
PDF Price: $15
Abstract
Dissolution is the concentration of a drug that goes into solution per unit of
time under standard conditions of solid-liquid interface, temperature, and composition
of solvent. In the pharmaceutical industry, in vitro dissolution testing has been
established as a preferred method to evaluate the development potential of new APIs
and drug formulations and to select the most appropriate solid form for further
development. Dissolution allows the measurement of some important physical
parameters, like drug diffusion coefficient, and is also used in model fitting on
experimental release data. Kinetic modeling of drug release in dosage forms has served
as a promising alternative to reduce bio studies in the development stage of
pharmaceutical formulations. Qualitative as well as quantitative changes in a
formulation that influence the performance of formulations can be predicted with the
help of different computational tools. The present chapter plans to highlight various
computational tools available online as well as offline such as PCP disso, DD solver,
Kinetds, etc. along with these software, the effective use of Microsoft Office Excel tool
for calculating drug kinetic studies is also discussed here.
Warp and Woof of Drug Designing and Development: An In-Silico Approach
Page: 280-294 (15)
Author: Monika Chauhan, Vikas Gupta, Anchal Arora, Gunpreet Kaur, Parveen Bansal and Ravinder Sharma*
DOI: 10.2174/9789815223019124010014
PDF Price: $15
Abstract
Designing and developing a novel therapeutic drug candidate remains a
daunting task and requires a long time with an investment of approximately ~USD 2-3
billion. Owing to the subpar pharmacokinetic or toxicity profiles of the therapeutic
candidates, only one molecule enters the market over a period of 12 to 24 years. So, the
reduction of cost, time, high attrition rate in the clinical phase, or drug failure has
become a challenging and dire question in front of the pharmaceutical industry. In the
last few decades, steep advancements in artificial intelligence, especially computeraided drug design have emerged with robust and swift drug-designing tools. Existing
reports have clearly indicated an imperative and successful adoption of virtual
screening in drug design and optimization. In parallel, advanced bioinformatics
integrated into genomics and proteomics discovering molecular signatures of disease
based on target identification or signaling cascades has directly or indirectly
smoothened the roadmap of the clinical trial. Integrated genomics, proteomics, and
bioinformatics have produced potent new strategies for addressing several biochemical
challenges and generating new approaches that define new biological products.
Therefore, it is fruitful to utilize the computational-based high throughput screening
methods to overcome the hurdles in drug discovery and characterize ventures. Besides
that, bioinformatic analysis speed up drug target selection, drug candidate screening,
and refinement, but it can also assist in characterizing side effects and predicting drug
resistance. In this chapter, the authors have discussed a snapshot of State-of-the-Art
technologies in drug designing and development.
Data Interpretation and Management Tools for Application in Pharmaceutical Research
Page: 295-312 (18)
Author: Arvinder Kaur*, Avichal Kumar, Kavya Manjunath, Deepa Bagur Paramesh, Shilpa Murthy and Anjali Sinha
DOI: 10.2174/9789815223019124010015
PDF Price: $15
Abstract
The information flow in pharmaceutical research before data interpretation
and management was largely manual and simple, with limited application of
technology. Establishing the research objective, designing the study, collecting data,
analyzing data, and interpreting the result were laborious, tedious, and time-consuming
processes. Manually entering and sorting a large amount of data made researchers more
prone to human errors, leading to incorrect and invalid results. The chapter draws on
data mining, data abstracting, and intelligent data analysis to collectively improve the
quality of drug discovery and delivery methods. To develop new drugs and improve
existing treatments, software can be used to analyze large datasets and identify patterns
that help understand how drugs interact with the body. Virtual models of organs and
cells are employed to study the effects of drugs, automate drug testing, and predict
adverse drug reactions. Pharmaceutical management tools, such as pharmacy
management software, electronic prescription software, inventory management
software, and automated dispensing systems, are highly valuable for managing
inventory, tracking patient prescriptions, monitoring drug interactions, maintaining
patient information and history, and providing up-to-date drug information. The main
objective of this chapter is to highlight the various tools and software solutions
available and how they can facilitate the research process to ensure compliance with
relevant regulations and laws regarding human healthcare safety.
Subject Index
Page: 313-318 (6)
Author: Dilpreet Singh and Prashant Tiwari
DOI: 10.2174/9789815223019124010016
Introduction
Software and Programming Tools in Pharmaceutical Research is a detailed primer on the use for computer programs in the design and development of new drugs. Chapters offer information about different programs and computational techniques in pharmacology. The book will help readers to harness computer technologies in pharmaceutical investigations. Readers will also appreciate the pivotal role that software applications and programming tools play in revolutionizing the pharmaceutical industry. The book includes nine structured chapters, each addressing a critical aspect of pharmaceutical research and software utilization. From an introduction to pharmaceutical informatics and computational chemistry to advanced topics like molecular modeling, data mining, and high-throughput screening, this book covers a wide range of topics. Key Features: - Practical Insights: Presents practical knowledge on how to effectively utilize software tools in pharmaceutical research. - Interdisciplinary Approach: Bridges the gap between pharmaceutical science and computer science - Cutting-Edge Topics: Covers the latest advancements in computational drug development, including data analysis and visualization techniques, drug repurposing, pharmacokinetic modelling and screening. - Recommendations for Tools: Includes informative tables for software tools - Referenced content: Includes scientific references for advanced readers The book is an ideal primer for students and educators in pharmaceutical science and computational biology, providing a comprehensive foundation for this rapidly evolving field. It is also an essential resource for pharmaceutical researchers, scientists, and professionals looking to enhance their understanding of software tools and programming in drug development.