Drug Delivery Systems using Quantum Computing (eBook)

Rishabha Malviya, Dhanalekshmi Unnikrishnan Meenakshi, Sonali Sundram (Herausgeber)

eBook Download: EPUB

2024 | 1. Auflage
480 Seiten
Wiley (Verlag)
978-1-394-15931-4 (ISBN)

Infos

The first book of its kind to show the potential of quantum computing in drug delivery.

Drug delivery systems (DDS) are defined as methods by which drugs are delivered to desired tissues, organs, cells, and subcellular organs for drug release and absorption through a variety of drug carriers. By controlling the precise level and/or location of a given drug in the body, side effects are reduced, doses are lowered, and new therapies are possible. Nevertheless, there are still significant obstacles to delivering certain medications to particular cells. Drug delivery methods change pharmacokinetic, pharmacodynamic, and drug release patterns to enhance product efficacy and safety, as well as patient convenience and compliance. Computational approaches in drug development enable quick screening of a vast chemical library and identification of possible binders by using modeling, simulation, and visualization tools. Quantum computing (QC) is a fundamentally new computing paradigm based on quantum mechanics rules that enables certain computations to be conducted significantly more rapidly and effectively than regular computing, and hence this has huge promise for the pharmaceutical sector.

Significant advances in computational simulation are making it easier to comprehend the process of drug delivery. This book explores an important biophysical component of DDSs, and how computer modeling may help with the logical design of DDSs with enhanced and optimized characteristics. The book concentrates on computational research for various important types of nanocarriers, including dendrimers and dendrons, polymers, peptides, nucleic acids, lipids, carbon-based DDSs, and gold nanoparticles.

Audience

Researchers and industry scientists working in clinical research and disease management; pharmacists, formulation and pharmaceutical scientists working in R&D; computer science engineers applying deep learning and quantum computing in healthcare.

Rishabha Malviya, PhD, is an associate professor in the Department of Pharmacy, School of Medical and Allied Sciences, Galgotias University. He has authored more than 150 research/review papers for national/international journals of repute. He has been granted more than 10 patents from different countries while a further 40 patents were either published or are under evaluation. His areas of interest include formulation optimization, nanoformulation, targeted drug delivery, localized drug delivery, and characterization of natural polymers as pharmaceutical excipients.

Sonali Sundram, PhD, MPharm, completed her doctorate in pharmacy and is currently working at Galgotias University, Greater Noida. Her areas of interest are neurodegeneration, clinical research, and artificial intelligence. She has edited 6 books, been granted 8 patents, and has organized more than 15 national and international seminars/conferences/workshops.

Dhanalekshmi Unnikrishnan Meenakshi, PhD, holds a doctorate in Pharmacology from the Council of Scientific and Industrial Research (CSIR)- CLRI, India. She is a faculty member in the College of Pharmacy, National University of Science and Technology, Muscat, Sultanate of Oman. She has published extensively in nanomedicine, drug delivery, and formulation technology in peer-reviewed reputed journals and books and works on an array of projects relating to cancer and gene therapy, nanotechnology, and pharmacology.

Textauszug

1
Quantum Computational Concepts and Approaches in Drug Discovery, Development and Delivery

Dhanalekshmi Unnikrishnan Meenakshi1*, Suresh Manic Kesavan2, Arul Prakash Francis3 and Shah Alam Khan1†

1College of Pharmacy, National University of Science and Technology, Muscat, Oman

2College of Engineering, National University of Science and Technology, Muscat, Oman

3Centre of Molecular Medicine and Diagnostics (COMManD), Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India

Abstract

The use of quantum computing approaches in drug discovery, development, and delivery is on rise. Quantum computing (QC) is becoming more popular as a cost-saving measure in the research, production, and manufacturing of drugs. The use of molecular dynamics and computation tasks has greatly enhanced drug design and optimization of drug delivery systems. This QC method aids in investigating several issues that are difficult to examine in laboratory studies. This chapter discusses how QC simulations can assign a specific drug target site by anticipating the target, resulting in a successful drug development process. It also highlights the important applications of several algorithms in drug delivery and disease diagnosis. Additionally, computational methodologies for various drug delivery systems and carriers are discussed. This chapter also discusses ongoing challenges in the pharmaceutical industry and concerns regarding the chances of accomplishment of QC concepts and technology.

Keywords: Drug discovery, drug delivery, quantum computing, simulations, optimization, molecular dynamics, algorithms

1.1 Introduction

Drug development and discovery to identify new chemical entities to treat or cure human diseases is a very long, complex, expensive, and tedious process that often fails [1]. Over the years, the scientific community has tried its best to decrease expenditure, speed up the process of drug development, and progress the accomplishment frequency of identifying drugs or molecules of natural or synthetic origin [2, 3]. In the late 20th century, the paradigm slowly shifted from Ethnopharmacology, a traditional discovery method from medicinal plants, to a computational chemistry-based technique called computer-aided drug design (CADD) to identify a lead molecule capable of binding to a target protein to exhibit favorable therapeutic effects and pharmacokinetic profile [4]. Discovery and optimization of lead molecules following the high-throughput screening (HTS) over hit libraries using a CADD approach can be done either by structured-based or ligand-based drug design [5]. While the latter is favored in the absence of target architectural data and uses a quantitative structure–activity relation (QSAR) model, the former approach is used when knowledge of the 3D structural information of the biological target is available for molecular docking (pharmacophore modeling). Although most of the drugs in the pipeline have been discovered using CADD, the higher computational cost to study the molecular dynamics of protein–ligand interactions and the reliability of the current statistical techniques available to study pharmacokinetic profile limits its usefulness [6–8]. The challenges and limitations of CADD could be overcome with the help of an emerging quantum computing (QC) technology that can handle and stimulate larger and more complex chemical structures more efficiently. QC application will certainly benefit the pharmaceutical industry from innovation in drug discovery to the manufacture and development of promising therapeutic modalities [8].

Constructing a new drug for a chronic illness in the medical context was mainly based on new pills. Various drugs for identifying energetic facets in conventional therapies like penicillin have recently been developed. In natural compounds, organic molecules that aid in medical purposes to discover ingredients such as cells or unbroken life forms are used in pharmaceutical manufacturing. This is known as traditional pharmacology. As the DNA sequence has enabled massive cloning techniques and improved protein refinement, HTS with various libraries has become more common. The therapy of disease by checking for large molecules using genetic goals is known as reverse pharmacology. By screening activity to supply cells, countless collisions can be obtained, and animal tests for adequacy have also been performed. Pipelines for pharmaceutical research are lengthy, complex, and dependent on a variety of factors. Additionally, QC is crucial to the creation of medication delivery systems. There are still problems that need to be tackled despite the vital role that QC has played in many stages of drug development and technological advancements. Precision parameters related to these virtual screening methods are constrained by energy- or similarity-based scoring concerns. Before receiving market permission, the virtual screening technique still needs to validate the compounds through preclinical and clinical tests.

Quantum computing may carry out difficult tasks like interpreting sensor data or analyzing complex interrelationships and variables that affect the quantity or reliability of carriers in medication delivery systems through the use of machine learning (ML) algorithms and artificial intelligence (AI) [9]. High-precision imaging of the produced formulation, particularly morphology, may be provided by QCs with very sophisticated processing and computing capabilities [10]. QC’s role in disease diagnosis is to create new interfaces between drug development, production, therapeutic effects, and diagnosis. Rapid cancer detection can be achieved using QCs and hence providing enhanced therapy. Besides, adverse effects can be reduced by devising precise radiation plans with an appropriate dose for targeting cancer cells [11]. Using Shor’s algorithm for data collection and creating novel datasets develop a novel method for cancer detection and the stage identification of various diseases seems to be relaxed [12]. High-precision radiation beams can be directed at cancer cells using QCs, and they can also anticipate TP53 gene alterations, which are crucial to the pathophysiology of several cancer types [13, 14]. With a significant amount of high-quality data, ML approaches provide a variety of techniques that can enhance innovation and strategic planning for some well-posed topics. ML applications are possible across the entire research process. Different applications have taken different approaches to their research, and most of these methodologies have produced reliable predictions and findings. Comprehensive and integrated high-dimensional data must still be derived in all areas.

As mentioned, drug discovery, development, and delivery are increasingly using QC techniques in diverse ways. The use of QC is growing in popularity as a way to produce and manufacture pharmaceuticals more cheaply. The design of drugs and the optimization of drug delivery systems have both been considerably improved by the use of molecular dynamics and computational tasks. Investigation of several topics that are challenging to evaluate in laboratory investigations is aided by this QC strategy. To achieve a successful medication development process, this chapter explores how QC simulations might assign a specific drug target location by predicting the target. It also emphasizes the significant roles that various algorithms play in the administration of medications and the identification of diseases. There is also a discussion on computational approaches for different medication delivery systems and carriers. This chapter also addresses persistent difficulties and challenges faced by the pharmaceutical industry.

1.2 Algorithms and QC in Pharma

The pharmaceutical industry revolves around the creation of molecular formulations that are then turned into pharmaceuticals to treat or cure diseases. As mentioned, the use of QC is growing in popularity as a way to produce and manufacture pharmaceuticals more cheaply. Pharmaceutical companies spend a whopping investment across all industries. For decades, pharmaceutical companies are the early users of digital tools like CADD, to improve the R&D process. Recently, AI has been used in pharmaceutical research and development and the next technological edge to focus on is QC. It has been stated that leaders in the pharmaceutical sector should respond to and discuss a set of critical concerns to choose the best course of action in the computation/simulation era.

1.2.1 Algorithms

Businesses are increasingly utilizing ML algorithms to make life easier to keep up with consumer expectations as the world becomes “smarter.” They can be found in consumer electronics (for example, facial acknowledgment for unlocking cellphones) or in the case of credit card fraud detection (such as setting up alerts for unusual purchases). Figure 1.1 illustrates the types of ML that are frequently used in research. AI and ML are divided into two categories: supervised learning and unsupervised learning.

1.2.2 Supervised Learning

It uses labeled datasets in supervised learning and this method is also a type of ML. The databases are employed to supervise processes so that they can correctly identify data or forecast results. The type can be tested for accuracy and learned over time by using labeled inputs and outputs.

Figure 1.1 Types of machine...

Produkt-Details

• Erscheint lt. Verlag: 2.7.2024
• Sprache: englisch
• Themenwelt: Naturwissenschaften ► Chemie
• ISBN-10: 1-394-15931-5 / 1394159315
• ISBN-13: 978-1-394-15931-4 / 9781394159314