Chapter 1 Drug development in virtual world: metaverse and digital twins in healthcare technology

ORCID:https://orcid.org/0009-0006-4779-2553

ORCID:https://orcid.org/0000-0003-2874-6149

Abstract

It is during the time of industrial revolutions that virtual reality becomes increasingly popular, particularly as industries tap into the vast possibilities this technology offers. Several applications have been witnessed so far, among them instances where virtual reality has had a decisive impact in medicine and pharmaceuticals. Within the pharmaceutical industry, virtual reality emerges both as a substitution and a supplementation of conventional pharmacotherapy. While still in its infancy in drug design, the conception of virtual reality within this sphere seems to have already made its mark. In fact, the application where virtual reality has clearly proved its worth appears to be in the education of pharmacists, where it has provided a more engaging and interactive learning experience that goes beyond comprehension and includes practical applications as well. Advanced technologies have also been observed to provide progressive momentum toward cheaper, portable, and flexible virtual reality systems, thereby improving access for both inpatients and outpatients. These advancements, it is also possible that interventions could complete therapy in a more efficient and entertaining manner. Although challenges remain, the world seems to be showing a growing interest in research into virtual reality and its ever-evolving capabilities. As a result, the scope and areas of application for virtual reality are likely to expand in the pharmaceutical and medical fields in the coming years.

1.1 Introduction

The paradigm of production has gone so far in making technological advancements available to different sectors to take the pharmaceutical industry to unprecedented innovation levels. One of such pioneering technologies that have played a part in bringing such lion's share of the innovation advancements is the virtual reality (VR) facility, which, in recent years, has gained much reputation across many sectors. Immersion in VR experiences was extended from entertainment avenues such as immersive video game experiences to incorporating various types of industry applications in healthcare to scientific research. The COVID-19 pandemic has further stimulated the use of VR in pharmaceutical research and development. During this global health challenge, researchers utilized VR to accelerate processes in drug discovery, guide the design of new therapeutic compounds, and enhance mechanisms for understanding diseases. As a result, VR has been absorbed into the rational drug design environment, which is a different computational approach that aids the outcome in developing effective and targeted pharmaceutical interventions. Well, these are some of the latest thave propelled VR to becoming an important facet of modern drug discovery and formulation [1].

Drug development will be another research area of virtual reality application that this paper will address with respect to its potential benefits for the pharmaceutical sector. The analysis of these contributions might shed light on how this technology has affected the different stages of drug design-from molecular modeling to clinical trials. In addition, this research intends to form the basis for future studies into the continuously evolving role of VR in pharmaceutical sciences, enabling further investigation of its applications and advancements. There have been significant advancements using VR in drug development for the pharmaceutical industry. Visualizing disease progression-in a much more holistic manner is also made possible by VR technology for scientists and researchers. It paves the way for innovative treatment development. The use of simulations in virtual reality encourages researchers to build safer and more efficient drug formulations by uncovering intricate biological interactions at a molecular level.

This research also aims to assess the different VR technologies that assist in drug design and optimize treatment strategies. As a case study, one can examine the role of the E-Brain virtual reality platform in drug delivery to the brain. Such newer technologies certainly help optimize the application of drugs for better absorption, diffusion, and therapeutic efficacy. Through the mechanism of integrating VR-based tools like E-Brain, pharmaceutical researchers will conduct better drug-targeting mechanisms and accelerate the drug development process while minimizing the time and costs associated with conventional approaches. By and large, one can argue that the introduction of VR in the pharmaceutical arena will mark significant advancements in modern medicine [2]. When the new state of technology develops, VR will play a greater role in drug discovery and facilitate drug development with precision, efficiency, reduced risk and lower expenditure. The findings of this study will lay a solid foundation for future research, thereby fostering innovative practices in the pharmaceutical industry.

1.2 Artificial intelligence in pharmaceutical industry

The last few years have seen a tremendous digital infiltration in the pharmaceutical industry, resulting in an unparalleled accumulation of data. However, this transformation brings with it a serious set of challenges in effectively gathering, analyzing, and using large amounts of information to solve complex clinical problems. Artificial intelligence (AI) has entered the scene as the most powerful tool to remedy this situation, enabling automated processing and management of big data. AI is a sophisticated technology-driven system that constitutes a variety of innovative tools and neural networks aimed at simulating some elements of human intelligence. Contrary to the common misinterpretation that AI can completely make human intervention redundant, its role is rather to assist decision-making and simplify intricate processes without excluding human supervision [3]. The systems that harness AI use advanced software and algorithms for the purpose of interpreting, learning from, and analyzing input data, thus converting the input into action to reach set goals in a highly efficient manner.

The area of AI in the pharmaceutical sector is always evolving, thereby creating disruptions in drug discovery, clinical trial viability, patient diagnostics, and personalized medicine. AI is uniquely positioned to instigate a major change in time-honored pharmaceutical processes through the use of machine learning (ML), deep learning (DL), and natural languages. McKinsey Global Institute metrics indicate that the advancing wave of automation with AI will soon set the stage for a fundamental reshuffle of workplace and societal patterns. As AI integrates into the fabric of the pharmaceutical industry, it stands ready to change the way research is conducted, improve the outcome of healthcare, and make operations much more efficient. That is the crux of a paradigm shift in development, emphasizing AI's role as a more important complement rather than a substitute for human competence [4].

1.3 AI in networks and tools

AI, quite a multi-disciplinary affair, includes reasoning, representation of knowledge, and resolution of problem strategies. ML is the basic paradigm that allows systems to discover patterns found in labeled datasets. Whereas ML uses different algorithms for processing and analyzing extensive datasets, thus improving machines' decision-making ability without any specific programming, a further specialized domain of machine learning (ML) focuses on deep learning (DL) and artificial neural networks (ANNs). These ANNs are built to simulate the workings of the human brain by emulating how biological neurons transmit electrical impulses.

ANNs consist of a structured set of nodes, each processing input signals and transforming them into output. These networks function through intricate computational mechanisms and are governed by algorithms that facilitate their learning and adaptation. ANNs are classified based on three criteria: structural configuration, learning mechanism, and application. Based on these criteria, multilayer perceptron (MLP) networks, recurrent neural networks (RNNs), and convolutional neural networks (CNNs) can all be distinguished. In terms of learning, either supervised or unsupervised learning is employed depending on the data and the problem being tackled [5]. MLP networks in supervised learning are widely used in fields such as pattern...