Deep Learning for Targeted Treatments E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
DEEP LEARNING FOR TREATMENTS
The book provides the direction for future research in deep learning in terms of its role in targeted treatment, biological systems, site-specific drug delivery, risk assessment in therapy, etc.
Deep Learning for Targeted Treatments describes the importance of the deep learning framework for patient care, disease imaging/detection, and health management. Since deep learning can and does play a major role in a patient’s healthcare management by controlling drug delivery to targeted tissues or organs, the main focus of the book is to leverage the various prospects of the DL framework for targeted therapy of various diseases. In terms of its industrial significance, this general-purpose automatic learning procedure is being widely implemented in pharmaceutical healthcare.
Audience
The book will be immensely interesting and useful to researchers and those working in the areas of clinical research, disease management, pharmaceuticals, R&D formulation, deep learning analytics, remote healthcare management, healthcare analytics, and deep learning in the healthcare industry.
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Seitenzahl: 779
Veröffentlichungsjahr: 2022
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Table of Contents
Cover
Series Page
Title Page
Copyright Page
Preface
Acknowledgement
1 Deep Learning and Site-Specific Drug Delivery: The Future and Intelligent Decision Support for Pharmaceutical Manufacturing Science
1.1 Introduction
1.2 Drug Discovery, Screening and Repurposing
1.3 DL and Pharmaceutical Formulation Strategy
1.4 Deep Learning Models for Nanoparticle-Based Drug Delivery
1.5 Model Prediction for Site-Specific Drug Delivery
1.6 Future Scope and Challenges
1.7 Conclusion
References
2 Role of Deep Learning, Blockchain and Internet of Things in Patient Care
2.1 Introduction
2.2 IoT and WBAN in Healthcare Systems
2.3 Blockchain Technology in Healthcare
2.4 Deep Learning in Healthcare
2.5 Conclusion
2.6 Acknowledgments
References
3 Deep Learning on Site-Specific Drug Delivery System
3.1 Introduction
3.2 Deep Learning
3.3 Machine Learning and Deep Learning Comparison
3.4 Applications of Deep Learning in Drug Delivery System
3.5 Conclusion
References
4 Deep Learning Advancements in Target Delivery
4.1 Introduction: Deep Learning and Targeted Drug Delivery
4.2 Different Models/Approaches of Deep Learning and Targeting Drug
4.3 QSAR Model
4.4 Deep Learning Process Applications in Pharmaceutical
4.5 Techniques for Predicting Pharmacotherapy
4.6 Approach to Diagnosis
4.7 Application
4.8 Conclusion
Acknowledgment
References
5 Deep Learning and Precision Medicine: Lessons to Learn for the Preeminent Treatment for Malignant Tumors
5.1 Introduction
5.2 Role of DL in Gene Identification, Unique Genomic Analysis, and Precise Cancer Diagnosis
5.3 DL in Next-Generation Sequencing, Biomarkers, and Clinical Validation
5.4 DL and Translational Oncology
5.5 DL in Clinical Trials—A Necessary Paradigm Shift
5.6 Challenges and Limitations
5.7 Conclusion
References
6 Personalized Therapy Using Deep Learning Advances
6.1 Introduction
6.2 Deep Learning
References
7 Tele-Health Monitoring Using Artificial Intelligence Deep Learning Framework
7.1 Introduction
7.2 Artificial Intelligence
7.3 AI-Enabled Telehealth: Social and Ethical Considerations
7.4 Conclusion
References
8 Deep Learning Framework for Cancer Diagnosis and Treatment
8.1 Deep Learning: An Emerging Field for Cancer Management
8.2 Deep Learning Framework in Diagnosis and Treatment of Cancer
8.3 Applications of Deep Learning in Cancer Diagnosis
8.4 Clinical Applications of Deep Learning in the Management of Cancer
8.5 Ethical Considerations in Deep Learning–Based Robotic Therapy
8.6 Conclusion
Acknowledgments
References
9 Applications of Deep Learning in Radiation Therapy
9.1 Introduction
9.2 History of Radiotherapy
9.3 Principal of Radiotherapy
9.4 Deep Learning
9.5 Radiation Therapy Techniques
9.6 Different Role of Deep Learning with Corresponding Role of Medical Physicist
9.7 Conclusion
References
10 Application of Deep Learning in Radiation Therapy
10.1 Introduction
10.2 Radiotherapy
10.3 Principle of Deep Learning and Machine Learning
10.4 Role of AI and Deep Learning in Radiation Therapy
10.5 Platforms for Deep Learning and Tools for Radiotherapy
10.6 Radiation Therapy Implementation in Deep Learning
10.7 Prediction of Outcomes
10.8 Deep Learning in Conjunction With Radiomoic
10.9 Planning for Treatment
10.10 Deep Learning’s Challenges and Future Potential
10.11 Conclusion
References
11 Deep Learning Framework for Cancer
11.1 Introduction
11.2 Brief History of Deep Learning
11.3 Types of Deep Learning Methods
11.4 Applications of Deep Learning
11.5 Cancer
11.6 Role of Deep Learning in Various Types of Cancer
11.7 Future Aspects of Deep Learning in Cancer
11.8 Conclusion
References
12 Cardiovascular Disease Prediction Using Deep Neural Network for Older People
12.1 Introduction
12.2 Proposed System Model
12.3 Random Forest Algorithm
12.4 Variable Importance for Random Forests
12.5 The Proposed Method Using a Deep Learning Model
12.6 Results and Discussions
12.7 Evaluation Metrics
12.8 Conclusion
References
13 Machine Learning: The Capabilities and Efficiency of Computers in Life Sciences
13.1 Introduction
13.2 Supervised Learning
13.3 Deep Learning: A New Era of Machine Learning
13.4 Deep Learning in Artificial Intelligence (AI)
13.5 Using ML to Enhance Preventive and Treatment Insights
13.6 Different Additional Emergent Machine Learning Uses
13.7 Machine Learning
13.8 Ethical and Social Issues Raised.... ! ! !
13.9 Future of Machine Learning in Healthcare
13.10 Challenges and Hesitations [73–75]
13.11 Concluding Thoughts
Acknowledgments
References
Index
Also of Interest
End User License Agreement
List of Tables
Chapter 1
Table 1.1 Applications of DL in drug discovery.
Chapter 2
Table 2.1 Machine learning models with their advantages and disadvantages ...
Table 2.2 Type of deep learning and algorithm with their application [85, ...
Chapter 4
Table 4.1 Different applications, methodologies and its benefits.
Chapter 5
Table 5.1 Public accessible large-scale cancer databases.
Chapter 6
Table 6.1 A list of CNN parameters and hyperparameters.
Chapter 8
Table 8.1 Machine learning application in diagnosis of various cancers.
Chapter 9
Table 9.1 Treatment areas and possible side effects of radiation therapy i...
Table 9.2 Summary of advantages and disadvantages of different radiation t...
Table 9.3 Volume and dose details of external beam radiotherapy [36, 37]....
Table 9.4 Some commonly used radioactive sources with their application [5...
Chapter 10
Table 10.1 An overview of the many deep learning-based applications, sorte...
Table 10.2 Deep learning for toxicity and result prediction in clinical co...
Chapter 11
Table 11.1 Cancer-causing factors.
Chapter 12
Table 12.1 Features imported into “df ” data form.
Table 12.2 UCI dataset for predicting heart disease.
Table 12.3 Variable representing feature importance.
Table 12.4 20 models comparison for accuracy training and testing.
Table 12.5 Performance metrics used for efficiency calculation in machine ...
Table 12.6 Detailed comparison of existing methods using heart disease dat...
Chapter 13
Table 13.1 Decision tree benefits and downsides are.
Table 13.2 SVM’s benefits and drawbacks are.
Table 13.3 Various other machine learning method with their features, adva...
Table 13.4 Benchmarks examples of AI applications for various groups [29–3...
List of Illustrations
Chapter 1
Figure 1.1 Role of DL in drug development and manufacturing.
Figure 1.2 Applications of AI/ML/DL in the design of pharmaceutical formul...
Figure 1.3 Categories of ML algorithms in learning relationship of input a...
Chapter 2
Figure 2.1 Various characteristics of IoT.
Figure 2.2 Use of IoT in healthcare system.
Figure 2.3 Some innovations used in IoT healthcare systems.
Figure 2.4 Schematic diagram of blockchain systems used in healthcare.
Figure 2.5 Schematic diagram represents blockchain medical applications.
Figure 2.6 Schematic diagram to represent different mobile application in ...
Chapter 3
Figure 3.1 Shows different deep learning models.
Figure 3.2 Difference in machine learning and deep learning methods [117, 11...
Figure 3.3 Different types of research data in pharmacy, which can be manage...
Chapter 4
Figure 4.1 Schematic diagram of drug targets predicted by the proposed met...
Figure 4.2 Illustrates a variety of deep learning applications.
Figure 4.3 Applications of AI in Pharmaceuticals.
Chapter 5
Figure 5.1 Applications for an effective clinical decision making in a hum...
Figure 5.2 Applications of DL in radiation oncology.
Figure 5.3 KG and its applications.
Figure 5.4 Significant advantages of ATP in radiation therapy.
Chapter 6
Figure 6.1 The hierarchy of terminology for AI, ML, and DL is depicted in ...
Figure 6.2 Approaches in DL.
Figure 6.3 Convolutional neural networks structure.
Figure 6.4 Multiplication of image matrix involving filter matrix and kern...
Figure 6.5 A stride with a value of two pixels as an example.
Figure 6.6 ReLU operation.
Figure 6.7 Max type pooling.
Figure 6.8 The step of flatting the matrix as FC layer.
Figure 6.9 Architecture of convolutional neural networks.
Figure 6.10 Input, hidden, and output layer of autoencoder network.
Figure 6.11 Restricted Boltzmann machine.
Figure 6.12 In reinforcement learning agent-environment interaction.
Figure 6.13 Physician approach and intervention on patient.
Figure 6.14 The general architecture of GAN.
Figure 6.15 Long short-term memory networks (LSTMs).
Chapter 7
Figure 7.1 Advantages of artificial intelligence.
Figure 7.2 Applications of AI in various domains.
Figure 7.3 Utilization of AI in telemedicine.
Figure 7.4 Benefits of using telemedicine.
Figure 7.5 Role of AI-assisted telemedicine in various domain.
Chapter 8
Figure 8.1 Types of machine learning.
Figure 8.2 Detection of cancer by deep learning.
Figure 8.3 Types of algorithms in diagnosis of various cancers.
Chapter 9
Figure 9.1 Schematic diagram represents the basic use/application of radio...
Figure 9.2 Schematic representation of clinical application of deep learni...
Figure 9.3 Schematic diagram of different types of radiation therapy techn...
Chapter 10
Figure 10.1 Radiotherapy pathway using deep learning.
Figure 10.2 AI framework for radiotherapy outcome prediction.
Figure 10.3 Deep learning trained data to predict the outcomes.
Chapter 11
Figure 11.1 Brief history of deep learning.
Figure 11.2 Applications of deep learning.
Chapter 12
Figure 12.1 Decision tree graph for maximum depth of 3.
Figure 12.2 Confusion matrix.
Figure 12.3 Decision tree with best hyperparameters.
Figure 12.4 Random forest with best hyperparameters.
Figure 12.5 Deep learning model.
Figure 12.6 Applied deep neural network model with 4096 neurons on the fir...
Figure 12.7 Sigmoid activation function range between 0 and 1.
Figure 12.8 Comparison of 20 different models for accuracy of testing, tra...
Figure 12.9 Feature importance of different variables.
Figure 12.10 Recall criteria for 20 models for train and test datasets.
Figure 12.11 RMSE criteria for 20 models for train and test datasets.
Chapter 13
Figure 13.1 Comparison between traditional and machine learning [7].
Figure 13.2 Machine learning and their types [8, 9].
Figure 13.3 Supervised learning workflow [10].
Figure 13.4 Decision tree.
Figure 13.5 Hyperplane to separate circle and square.
Figure 13.6 A neuron.
Figure 13.7 Structure of an artificial neural networks (ANNs).
Figure 13.8 A outline of the areas of artificial intelligence.
Figure 13.9 Schematic representation of patient journey.
Guide
Cover Page
Series Page
Title Page
Copyright Page
Preface
Acknowledgement
Table of Contents
Begin Reading
Index
Also of Interest
WILEY END USER LICENSE AGREEMENT
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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Deep Learning for Targeted Treatments
Transformation in Healthcare
Edited by
Rishabha Malviya
Gheorghita Ghinea
Rajesh Kumar Dhanaraj
Balamurugan Balusamy
and
Sonali Sundram
This edition first published 2022 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2022 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
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Library of Congress Cataloging-in-Publication Data
ISBN 9781119857327
Cover image: Pixabay.ComCover design by Russell Richardson
Preface
Given the digital availability of knowledge today, deep learning (DL) has become a hot topic in the field of medicine in recent years. Deep learning is the general-purpose automatic learning procedure that is currently being widely implemented in a number of fields, including science, industry, and government. Since pharmaceutical formulation data consists of formulation combinations and methodological approaches that are neither image nor sequential data, this fully connected broad feed-forward network is a good option to predict pharmaceutical formulations. Moreover, targeted delivery of drugs to diseased tissues is another major challenge that can be solved by utilizing the deep learning framework. This book describes the importance of this framework for patient care, disease imaging/detection and health management. Since deep learning can play a major role in a patient’s healthcare management by controlling drug delivery to targeted tissues or organs, the main focus of the book is to leverage the various prospects of the DL framework for targeted therapy of various diseases. In terms of its industrial significance, this general-purpose automatic learning procedure is being widely implemented in pharmaceutical healthcare.
This book provides the direction for future research in deep learning in terms of its role in targeted treatment, biological systems, site-specific drug delivery, risk assessment in therapy, etc. The profusely referenced and copiously illustrated 13 chapters are subdivided into various sections that were written by renown researchers from many parts of the world. It should be noted that since all chapters were deliberately reviewed and suitably revised once or twice, the information presented in this book is of the highest quality and meets the highest publication standards. Therefore, this book should be both immensely interesting and useful to researchers and those in industry working in the areas of clinical research, disease management, pharmaceuticals, R&D formulation, deep learning analytics, remote healthcare management, healthcare analytics, and deep learning in the healthcare industry.
Finally comes the best part, which is to thank everyone who helped to make this book possible. First and foremost, we express our heartfelt gratitude to the authors for their contributions, dedication, participation, and willingness to share their significant research experience in the form of written testimonials, without which this book would not have been possible. Lastly, we want to express our gratitude to Martin Scrivener of Scrivener Publishing for his unwavering support.
The Editors
July 2022
Acknowledgement
Our sincere thanks to
Prof. P. K. Sharma
Pro-VC
Galgotias University
Without his encouragement and support
This task wouldn’t have been possible
Having an idea and turning it into a book is as hard as it sounds. The experience is both internally challenging and rewarding. At the very outset, we fail to find adequate words, with limited vocabulary to our command, to express our emotion to almighty, whose eternal blessing, divine presence, and masterly guidelines helps us to fulfill all our goals.
When emotions are profound, words sometimes are not sufficient to express our thanks and gratitude. We especially want to thank the individuals that helped make this happen. Without the experiences and support from my peers and team, this book would not exist.
No words can describe the immense contribution of our parents, friends, without whose support this work would have not been possible.
Last but not least, we would like to thank, our publisher for their support, innovative suggestions and guidance in bringing out this edition.
1Deep Learning and Site-Specific Drug Delivery: The Future and Intelligent Decision Support for Pharmaceutical Manufacturing Science
Dhanalekshmi Unnikrishnan Meenakshi1*, Selvasudha Nandakumar2, Arul Prakash Francis3, Pushpa Sweety4, Shivkanya Fuloria5, Neeraj Kumar Fuloria5, Vetriselvan Subramaniyan6 and Shah Alam Khan1†
1College of Pharmacy, National University of Science and Technology, Muscat, Oman
2Department of Biotechnology, Pondicherry University, Puducherry, India
3Department of Biochemistry and Molecular Biology, Pondicherry University, Puducherry, India
4Anna University, BIT Campus, Tiruchirappalli, India
5Faculty of Pharmacy, AIMST University, Bedong, Malaysia
6Faculty of Medicine, Bioscience and Nursing, MAHSA University, Selangor, Malaysia
Abstract
Site-specific drug delivery [SSDD] is a smart localized and targeted delivery system that is used to improve drug efficiency, decrease drug-related toxicity, and prolong the duration of action by having protected interaction between a drug and the diseased tissue. SSDD system in association with the computational approaches is employed in discovery, design, and development of drugs to improve treatment outcomes. Artificial intelligence [AI] networks and tools are playing a prominent role in developing pharmaceutical products by employing fundamental paradigms. Among many computational techniques, deep learning [DL] technology utilizes artificial neural networks [ANN], belongs to machine learning [ML] approach that holds the key to measuring and forecasting a drug’s affinity for specific targets. It can reduce both cost and time by speeding up the drug development process rationally with careful decisions. DL is considered as the primary strategy to predict bioactivity as it shows improved performance compared with other technologies in the field. DL can assist in evaluating the success of a target-based drug design and development before the actual laboratory synthesis or production of the drug molecule. This chapter highlights the potential applications of DL in assigning a specific drug target site by predicting the structure of the target protein and drug affinity for a successful treatment. It also spotlights the impactful applications of many types of DL in SSDD and its advantages over conventional SSDD systems. Furthermore, some formulations that are intended to lead to the target or site-specific delivery and DL role in docking and pharmacokinetics profiling are also addressed. Ongoing challenges, skepticism about the likelihood of success, and the paths to overcome by future technological advancements are also dealt with briefly. Due emphasis is given to the use of DL in reducing the economic burden of pharmaceutical industries to overcome costly failures and in developing target specific new drug candidate[s] for a successful therapeutic regimen beneficial to human life.
Keywords: Site-specific, target, drug delivery, deep learning, machine learning, artificial intelligence, computational approach, precision medicine
1.1 Introduction
Site-specific drug delivery (SSDD) is an almost a century-old strategy but successful delivery of drugs to the target site without producing off-site unwanted adverse effects has not been realized yet. Random testing assays in the traditional development of SSDD identify only 3% of compounds that warrant further laboratory tests, and hence, it is vital to explore the drug-target interactions for every single pharmaceutical molecule. Modern drug discovery, which includes identifying and preparing drug-molecular targets with precision, is emerging to fill traditional SSDD gaps. Target-specific drug delivery promotes the delivery of medications to target sites without creating unwanted side effects elsewhere. Despite numerous publications and attention paid to the site-specific delivery that promises to “deliver” the medicine at the diseased site, the generation of target-specific therapeutic products has still been a challenge for researchers [1]. The obstructions met during the drug formulation process are mainly associated with the inability to foresee the impact of the combination of active pharmaceutical ingredients [APIs] and materials on the formulation parameters. A new drug formulation development process and the associated procedures need to satisfy the site-specific delivery and release profile. Moreover, it is a laborious task and the protocols to perform in vitro characterizations or modifications to obtain the desired profile are difficult for the formulators [2]. To bridge the knowledge gap and reduce the time required for selecting the best molecule for drug development, researchers have devised computational modeling approaches like molecular dynamics simulations, docking studies [3], and cheminformatics [4]. These helps in the evaluation of novel insights about the complex drug delivery systems, especially in atomic/cellular scale which experimental techniques cannot provide [5–7]. A revolution in data science has been observed in the last decades due to the usage of the graphics processing unit [GPU]. A large volume of drug-related data and techniques were generated and analyzed using artificial intelligence [AI] to predict drug interaction with the diseased targets in drug discovery. AI networks and tools are playing a prominent role in the development of pharmaceutical products by employing fundamental paradigms. In medicinal chemistry, several computational methods contributed to designing new drug candidates by relating the drug candidate’s physicochemical properties, biological activity, and binding affinity [8]. Machine learning [ML], the branch of AI, has gained importance in drug discovery protocols and has become the most attractive and prominent research areas. ML supports the advancement of effective formulation through data-driven predictions using experimental data. A well-designed ML technique can significantly speed up the optimization of formulations with reduced cost [9]. Knowledge acquisition about the molecular characteristics of lead molecules has been made with the help of ML techniques like partial least squares [PLS], k-nearest neighbors [kNN], and artificial neural networks [ANN] [10]. ANN is the most prevalent ML technique in formulation prediction [9, 10].
Among the various methodologies of AI, deep learning [DL] had gained significant attention in several areas because of its ability to extract features from data [11]. Leading pharmaceutical industries in collaboration with different AI organizations are trying to develop effective and ideal drug candidates in the field of oncology and CNS complications. In recent years, several trials involving the combination of nanotechnology and DL are underway to study their potential role in drug formulation with SSDD. The role of DL in drug development and manufacturing is depicted in Figure 1.1. DL methods are representation-learning techniques that can discover multiple-level representations of increasing complexity from the raw data using nonlinear models [12]. Several recent trials have connected nanotechnology and DL to study their potential role in drug formulation with site-specific drug delivery [SSDD]. DL can predict the probable drug carrier candidate through target-based drug designing and development. DL methods play a significant role in drug delivery by predicting (i) drug loading in the carrier, (ii) the enhancement in permeability through the body barriers, and choosing the stable drug delivery systems from different carriers and matrices [13].
Figure 1.1 Role of DL in drug development and manufacturing.
DL has proved to be an effective tool for virtual screening and predicting quantitative structure-activity relationships from large chemical libraries [14]. Golkov et al. reported that the DL is very useful in predicting the biological functions of several chemical compounds from the raw data based on their electronic arrangements [15]. A previous study on DL revealed that it has collected evidence from the vast amount of data sets related to the genome and utilized for drug repurposing and precise treatments [16]. Various DL models have been used to forecast interactions between protein-ligand, scoring docking poses, and virtual screenings. Thus, DL has been utilized to discover several endpoints in medicinal chemistry [17].
A study on predicting protein-ligand interactions using molecular fingerprints and protein sequences as vector input showed that the essential amino acid residues responsible for drug-target interactions were predicted using vectors obtained from the model [18]. A previous study by Lee et al. detailed a predictive model to represent the DeepConv-drug-target interactions [DTIs] in ligand-target complex. The predictive models were built using over 32,000 drug-target structures from the DrugBank, IUPHAR, and KEGG data sets. DNN outperforms similarity-based models and traditional protein representations, according to the findings [19]. For the prediction of novel DTIs between marketed medications and targets, Wen et al. used a successful DL method called deep belief network [DBN] and developed a methodology called DeepDTIs. This method was tested using an appropriate method and associated to suitable algorithms, such as random forest [RF], Bernoulli Naive Bayesian [BNB], and decision tree [DT]. Results showed that the algorithm used in this method achieves comparatively high prediction performance and could be used for drug repositioning in the future [20]. Another study proved that DNN surpasses support vector machines [SVM] used internal testing to predict the drugs and therapeutic categories after ten-fold cross-validation using gene expression data [16]. Recently, the combination of DL-based predictors with the conformal prediction framework to create highly extrapolative models and further evaluated their performance on toxicology in the 21st century [Tox21] data [21]. The results suggested that the utility of conformal predictors is an appropriate way to provide toxicity predictions with assurance. Another study introduced QuantitativeTox (a DL-based framework) to predict toxicity endpoints like LD50, IGC50, LC50, and LC50-DM [22].
Many researchers reported the applications of DL in drug design using different models. The development of new applications and methodologies makes this system a reliable tool in the collections available to discover new drug candidates. In terms of drug discovery and development, DL techniques still have a long way to go and the applications of DL methods in target-specific drug delivery are focused on in this chapter. It also discusses how DL can be used to assign a specific drug target site by estimating the target protein’s structure and drug affinity for successful therapy. It also highlights the important applications of DL in SSDD, as well as the benefits of DL over traditional SSDD systems. Furthermore, some formulations intended to lead to the target or site-specific delivery and DL role in docking and pharmacokinetic profiling are also addressed. Ongoing challenges, skepticism about the likelihood of success, and the paths to overcome by future technological advancements are also dealt with. The application of DL in minimizing the economic burden of pharmaceutical enterprises to overcome costly failures and produce target-specific new drug candidate[s] for a successful therapeutic regimen beneficial to human life is discussed briefly in this chapter.
1.2 Drug Discovery, Screening and Repurposing
Drug discovery is a complex, tedious, lengthy, costly, and challenging multistep process with a very high failure rate. That is why a new drug approximately requires 10 to 15 years to enter from the bench to the bedside. Despite rapid development in the field of chemical and biological sciences, only an average of 25 new molecular entities [NME] per year were approved over the last two decades, indicating obvious challenges and obstacles associated with the currently used methods for the discovery of drugs [23]. In the late 20th century, target-based drug discovery programs [TBDD] focused on the identification of promising target proteins trailed by high-throughput screening [HTS] to recognize potential drug candidates based on their interaction with the target protein. However, HTS screening program is a costly, time-consuming process with low success rates. Therefore, pharmaceutical industries during 2001 to 2020 mainly relied on virtual screening [VS], i.e., in silico computational methods to design and discover new drugs that resulted in the approval of 498 NME by the US-FDA. VS predicts drug-target interaction and is carried out just before the HTS to increase success rate with less cost. One study reported that the hit rate to identify a suitable protein tyrosine phosphatase-1B enzyme inhibitor by VS approach was much higher [34.8%] than the HTS method [0.021%] [24]. The chemical [small bioactive molecules] and biological [protein target structures] databases are expanding at a rapid pace [high volume, velocity, and variety] due to advancements in technology. To speed up drug discovery, DL seems to be a popular approach for mining suitable drug targets from big data. DL is helpful in drug discovery process, prediction of physicochemical characteristics, quantitative structure-activity relationship [QSAR] studies, bioactivities, ligand-based and structure-based virtual screening, toxicity, mechanism of action, drug-target interaction, protein-protein interactions, design of dosage form, etc. Zhavoronkov and co-workers in 2019 used DL method namely generative tensorial reinforcement learning [GENTRL] and discovered potent inhibitors of discoidin domain receptor 1 [DDR1] merely in 3 weeks. One of the inhibitors showed promising activity against fibrosis and a favorable pharmacokinetic profile in experimental animals [25]. Stokes et al. in 2020 discovered a broad-spectrum antibiotic halicin employing a DNN model of DL. The chemical structure of bactericidal halicin is very different from the core of existing antibiotic molecules and was identified from the drug-repurposing hub through the prediction of antibacterial activities [26].
DL uses chemical and protein data to accelerate the drug design and development protocols. The big data in chemical space are stored in databases, such as ChemSpider, ChEMBL, ChemMine, ChemBank, DrugCentral, GDB-17, ZINC, and PubChem, while 3D images of proteins are available in protein data bank PDB, BindingDB, and KEGG ligand. DisGeNET database provides useful information on the relationship between human disease-associated genes and variants [27]. Another important database for drug discovery is STITCH, which provides information on interactions between small chemical molecules and target proteins along with binding affinity [28]. For drug discovery, DL employs several subsets of ANNs, including deep neural networks [DNNs], recurrent neural networks [RNNs], and convolutional neural networks [CNNs]. DNN can be used either to generate the structure of bioactive compounds from the pool of chemical libraries and training sets [generative DNN] or to predict physicochemical properties of novel bioactive chemicals [predictive DNN] [29, 30]. RNNs are primarily used to process sequential data. It works on a self-learning method and helps to create a descriptive simplified molecular-input line-entry system [SMILES] for characterization and synthesis of molecules [4, 31]. CNNs are the most effective tool of DL that can convert 2D to 3D data. CNN is used to differentiate data for the identification of gene mutations, disease target, lead candidate, and their interaction based on microscopy images and fingerprints. It is a very good DL algorithm for handling 2D data but requires a long training time [32–34]. In the recent past, graph neural networks [GNNs] are preferred over RNNs and CNNs that present model data in a graph instead of representation in Euclidean space [35]. GNN molecular graphs for small bioactive molecules are a useful drug development process to predict molecular attributes and generate molecular tasks [36].
QSAR modeling is a computational technique used in drug discovery. It develops a quantitative relationship between the physiochemical features of tiny chemical compounds and their biological activities using mathematical models. Some of the web-based models developed for QSAR studies include; Cloud 3D-QSAR, FL-QSAR, QSAR-Co-X, Meta-QSAR, and Vega platform, etc., [37]. AlphaFold is an AI-based tool developed by Google’s DeepMind to identify protein interaction [38]. This CNN-based tool can help in structure-based VS for drug discovery. Al Quraishi in 2019 also developed a similar DL-based tool, known as Recurrent Geometric Network, for the prediction of 3D structures of proteins [39].
The Monte Carlo tree search (MCTS) technique is a computational-based NN system and is very effective in generating various chemical synthetic pathways and in providing a solution to the total synthesis problems [40]. AiZynthFinder is recently developed by Genheden et al., using MCTS approach for retrosynthesis planning [41]. DeepScreening is a DL algorithm-based, user-friendly online server developed by Liu et al., in 2019 for drug discovery. It assists in VS of chemical compounds either from the public database or as defined by the user for a particular target protein [42]. The deep Reinforcement Learning for Structural Evolution [ReLeaSE] program, which is based on the stack-augmented RNN, could be used to develop chemical libraries. ReLeaSE performs the de novo drug design through generative and predictive DNNs [43, 44]. Bai et al. in 2020 designed a soft tool called MolAIcal to design 3D drugs in 3D protein pockets. It utilizes DL and genetic algorithms for de novo drug design using US- FDA-approved drugs followed by DL-based molecular docking [45]. Drug discovery applications of DL are briefly presented in Table 1.1.
As discussed, conventional drug design and development might take a long period, expensive, off-target delivery, and high risk, with enormous difficulties and challenges; as a result, efforts are made to repurpose existing medications [60, 61]. Drug repurposing [or drug repositioning] is an approach that helps to speed up the applications of an already approved existing drug for a new indication, thus reducing the difficulties of discovering new drug molecules [62]. The advancement in the large-scale, heterogeneous biological networks provided unique opportunities in in silico drug repurposing methods as discussed elsewhere in this chapter [60]. These appealing properties have piqued biopharmaceutical companies’ interest in scanning existing medications for potential repurposing applications. According to an estimate, approximately 30% of FDA-approved new drug products were made available through medication repurposing [63]. Using various biological networks, significant data collection from molecular, genomic, and phenotypic data facilitates the advanced development in drug repurposing [62]. Mechanism-based repurposing approaches are likely to find new indications for individual patients, given the current demand for PM and personalized therapy. These approaches consider the patient’s complexity and heterogeneity, lowering the risk of drug toxicity and interpatient variability therapeutic efficacy [62].
Computer-assisted drug repositioning plays a leading role to improve the safety and efficacy of repurposing approaches utilizing the advantage of computational modeling through the data obtained from preclinical and clinical studies. With the advancement of computational drug design, various anticancer drugs, like Gefitinib, Erlotinib, Sorafenib, Crizotinib, and so on, were profitably discovered, which has been considered a milestone in this area. Collaboration of computational and AI methods are creating new promising outcomes in drug development research, and the role of DL is valued by pharma industries [37, 64]. DL creates a unique perspective on how drug molecules bind to target molecules, the changes in their physicochemical characteristics that result, and how these changes impact phenotypic alterations. Furthermore, this technique aids in the identification of novel therapeutic targets from large-scale data sets gathered by numerous programs [65].
Table 1.1 Applications of DL in drug discovery.
Method
Application
Purpose
Ref
Undirected graph recursive neural networks [UGRNNs]
Ligand-based approach
Prediction of solubility (aqueous) of organic compounds
[
46
]
DNNs
Ligand-based approach
Prediction of binary toxic effects using Tox21 Data Challenge
[
47
]
UGRNNs
Ligand-based approach
Prediction of drug induced liver injury
[
48
]
Molecular graph convolution DNNs
Ligand-based approach
Prediction of binary toxic effects using Tox21 Data Challenge
[
49
]
CNNs and Random forest [RF]
Ligand-based approach
Prediction of Tox21, SIDER, and MUV data sets
[
50
]
Graph convolutional DNNs
Ligand-based approach
Prediction of bioactivity and toxicity using Tox21, MUV, and PubChem BioAssay data sets
[
51
]
Convolutional 3D layer DL method
Structure-based approach
Prediction of structural features of a pharmacophore evaluated by AutoDock Vina score
[
52
]
DeepVS- DNNs
Structure-based approach
To identify active compounds from inactive using molecular descriptors.
[
14
]
DL architecture with four hidden layers
Ligand-target interaction prediction
Prediction of binding affinity of ligand-protein target interactions
[
53
]
DBN models
Ligand-target interaction prediction
Prediction of drug target interactions were better than RF and BNB.
[
20
]
CNN and GNN
Ligand-target interaction prediction
Prediction of protein residues at the binding site
[
18
]
DeepConv-DTI
Ligand-target interaction prediction
Prediction of binding affinity
[
19
]
RNNs combined with reinforced learning
Chemical synthesis
De Novo
drug design of bioactive compounds
[
54
,
55
]
Transformer-CNN
QSAR modeling
QSAR modeling
[
56
]
A dual CNN
Drug repurposing
Disease-drug association via Chou’s five-step rule
[
57
]
Graph NN based DeepCE
Drug repurposing
Prediction of the differential gene expression profile and to identify novel lead compounds through drug repurposing
[
58
]
Rotation Forest and DNNs
Ligand-based approach
Prediction of QSAR models to identify dipeptidyl peptidase-4 [DPP-40 inhibitors
[
59
]
Drug candidates for medical repurpose could be ordered using information from the biological literature and databases. The information was then transferred to several sources, allowing it to be incorporated into a knowledge graph [KG]. As a result, it covers all known links between biomedical concepts including medications, diseases, genes, and so on [66]. Constructing drug KG is a key pace to utilize existing and discrete drug information [67]. These graphs, which are made up of nodes and edges, represent biomedical concepts and relationships and can aid researchers in solving a variety of issues, assisting in patient diagnosis, and establishing links between diseases and drugs [68]. Moridi et al. reported the advantages of the DL technique to extract the drug and disease features and explore their logical relationship in drug repurposing [69]. Such techniques are used to a wide range of data, including genomics, phenotypic statistics, and chemical statistics [67]. By using good drug data representation techniques, KG aids in converting knowledge into useful inputs for ML algorithms that may accurately forecast drug repurposing possibilities.
1.3 DL and Pharmaceutical Formulation Strategy
1.3.1 DL in Dose and Formulation Prediction
Even though there is a variety of successful pharmaceutical formulations with efficient delivery, inappropriate dose recommendation to a patient is the key reason for most adverse reactions and toxicity. Therefore, it is essential to regulate the administration of right dose for treating specific diseases. For more than a few decades, the determination of the optimum dose of a drug to accomplish the preferred and successful pharmacological action with the least toxicity is a challenging task [70]. The most widely used ML technology in pharmaceutical formulation prediction is ANN. DL algorithms could be applied to determine proper drug doses with minimal toxicity. Telemetry observation of the antiarrhythmic medication dofetilide has been done for 3 days because of its intensified toxicity risk. Levy et al. studied the ML algorithms role and dofetilide dose adjustment patterns for successful commencement of the medication and its dose prediction [71]. A reinforcement learning algorithm that is familiar with unsupervised learning can predict dosing decisions with an accuracy of 96.1%. A data-driven prediction system using an ML technique proficient of modeling pathogen-drug dynamics and projecting efficiency of dosage fixing and medication administration systems were developed. Using metronidazole and Giardia lamblia, the approach was confirmed for cell-drug interaction, with an accuracy of 85% [72].
Ter-Levonian and Koshechkin analyzed articles on ML and DL and reported that DL has the competence to select the required dosage regimen and can decide the proper combinations for any treatment strategies [73]. Drug Synergy Combinations strongly prove that DL approach is the best for dose prediction and therapeutic action. CoSynE and INferring Drug Interactions using chemoGenomics and Orthology (INDIGO) algorithms are employed for the synergistic combination selection of antibacterial agents. INDIGO identified that the combination of antagonistic antibiotics moxifloxacin and spectinomycin could turn into extremely synergistic by adding Clofazimine as a third drug. Comparison between DeepSynergy approaches and a few other ML approaches like elastic nets, gradient boosting machines, RF, and SVM evidenced the implication of deep synergy over other methods for envisaging novel therapeutic interactions of drugs and cell lines that have been studied. In the NCI ALMANAC database, 2620 medication combinations were evaluated in 60 cancer cell lines, yielding 3.04 million data points. In vitro, synergistic drug interaction between Navelbine and Iressa in the SK-MEL-5 melanoma cell line was also confirmed by Ter-Levonian and Koshechkin [73].
Treatments for many life-threatening diseases are done by prescribing/ dispensing multiple drugs and it has become a routine practice especially in cancer treatment. In contrast to single-agent trials, finding a dose for combinatorial drugs poses various encounters. Lin and Yin proposed a novel Bayesian adaptive drug-combination trial design based on a resilient dimension-reduction algorithm [74]. Weight, height, and age are the key factors for precise calculation of dose to avoid toxicity primarily in children. Pediatricians have piloted endless wide-ranging medical literature reviews and data to gather proof-based drug dosage data and to deliver a platform that recommends drug dosing. Rodle et al. defined the design of a model for the recommendation of pediatric drug selection and dosing based on clinical medication data. They launched an Extract-Transform-Load (ETL) process to offer data for ANNs, which includes patients’ age, weight, and full medication characteristics (e.g., dosage and route of application) and emphasis on three active substances, namely paracetamol, ibuprofen, and cefotaxim. The genetic algorithm with backpropagation has reached the maximum accuracy among other learning algorithms [75]. The transplantation of organs is a major source of apprehension in the medical industry, especially liver and kidney transplantation as it is very vital for their survival. For transplantation protocol, tacrolimus (an immunosuppressant drug) is generally prescribed for suppressing immunity. DNN enables the optimization of these kinds of drugs concerning the characteristics of patients. This strategy improves the ability to overcome numerous risk factors by properly predicting the formulation requirements of tacrolimus for organ transplants in a tailored manner. This prediction technique will take into account minute details that influence tacrolimus dosage variance [76]. Boosted regression trees, Bayesian additive regression trees, multivariate adaptive regression splines are other ANN approaches to decide the optimal dose of tacrolimus [77].
Antibiotics are essential in the cure of various ailments, but clinicians observed failures of antibiotics secondary to bacterial resistance. The advanced tools of DL are reported to address these issues. At Hokkaido University Hospital, a study was conducted by enrolling 654 patients with the rationale to develop an algorithm for primary dose sets of vancomycin [VCM] and was called VCM decision tree [DT] analysis. VCM daily doses calculated by DT algorithm ranged from 20.0 to 58.1 mg/kg while with nomogram range of 15 to 40 mg/kg with eGFR ≥50.13. As a result, the amounts suggested by the DT are higher than nomogram, which tend to be underdosing. Therefore, it was concluded that ML is beneficial for dose fixation and DT algorithm attained the maximum therapeutic range for vancomycin in comparison to conventional methods of dose setting [78]. Prediction of the dose was performed using 40 patients with the Bayesian network [BN] and compared with body weight-adjusted doses calculation. The BN seems to be an optimal approach to estimate the first dose of amikacin and proves its probable utilization for other antibiotics doses calculation, which was not involved in clinical practice blood parameter detection [79].
AI-PRS, an AI-based platform, was introduced by Shen et al. for selecting optimal doses and combinations of drugs for antiretroviral therapy [HIV] [80]. It is an NN-driven method, which links efficacy to drug dosage and its combinations by employing a parabolic response curve [PRS]. Using the PRS method, a combination of antiviral drugs, including efa-virenz, lamivudine, and tenofovir, was administered to 10 HIV patients. The starting dose of tenofovir decreased by 33% without causing virus relapse, and it was concluded that AI-PRS optimal drug dosage platform can also be conveniently applied to other ailments. DL has extensive application in screening the dose of cancer drugs and radiation therapy. Pantuck et al. established CURATE. AI to guide the selection of optimum drug dose from the patient’s data records. A combination of ZEN-3694, an investigational drug and chemotherapeutic drug enzalutamide, was administered to a patient with prostate cancer [81]. Applying CURATE. In AI, it was discovered that a 50% lower dose of ZEN-3694 than the beginning dose is adequate to stop cancer development. Lu et al. suggested a DL method created using neural ordinary differential equations [neural-ODE] and concluded that neural-ODE is the perfect pharmacokinetic tool for the prediction of untested treatment regimens prediction [82]. Timing and dosing data are incorporated directly at the decoder stage to enable the adoption of this method to a different treatment regimen. This model will have future scope in in vitro/in vivo extrapolation and pharmacokinetic profiling. DeepDose is a DL-based approach for rapid dose calculations in radiation therapy [83]. An ML approach based on dose demands was used to detect the ideal starting dose of the anticoagulant warfarin [84]. Similarly, a perfect dose of heparin is obtained with the help of a deep reinforcement learning approach [85]. ML approaches, like multilayer perceptron network, classification, regression trees, and k-nearest neighbor, were used by Hu et al. to establish a safe beginning dose of the cardiac medication digoxin [86]. Zhu et al. used noninvasive quantifiable features to validate the amount [C/D ratio] of lamotrigine by employing a dose-adjusted ML approach. For drug monitoring, 15 ML models were optimized using an extra tree regression algorithm, and the results of these studies can direct the clinicians for suitable dose adjustment in patients to minimize adverse reactions and can be conveniently used for other drugs in the future [87].
Using AttPharm, drug formulation data sets among eight groups of cyclodextrins [CD] and 1320 different ranges of distinct molecules were retrieved through academic literature published between 1990 and 2018. AttPharm used representation learning to manage the feature values and physical meaning separately. AttPharm came in three different flavors: AttPharm, AttPharm-formula, and AttPharm-formula-ResNet. The AttPharm-formula-ResNet was used to estimate the CD binding-free energy, which also took into account weight distributions that may be characterized as feature level, as well as sample level interpretability. Preparation into CD inclusion complexes can increase insoluble drug solubility and bioavailability, improve drug stability, mask undesirable odors, and lessen medication irritation and side effects. The findings revealed that in the pharmaceutical data set, lipophilic contact among sender and the receiver compounds, steric hindrance, testing temperature, and hydrogen bonds all have a substantial influence on the development of CD complexation [88]. This is the starting point toward developing pharmaceutical formulations utilizing attention-based DNNs. Several pharmaceutical and nutrition operations can potentially benefit from the proposed strategy.
Because of inadequate experimental data, ANNs’ formulation prediction accuracy is low. Pharmaceutical data in formulation consists of various compositions and production procedures, which are neither visual nor sequence data. As a result, the fully-connected deep feed-forward network is a nice option regarding pharmaceutical formulation prediction. DNNs beat ANNs with one hidden layer in the prediction of orally disintegrating tablet, according to a recent study [89]. Further comparisons of DL with other ML approaches are required to predict good formulations. The small data set with imbalanced input space is one of the most difficult formulation prediction elements. The data splitting algorithm and assessment parameters appropriate for pharmaceutical formulation data sets should be examined for improved performance. DNNs [SRMT] were trained using the data. Compared with other ML methods, DL can uncover the complex relationship among formulations and their in-vitro characteristics, indicating that DL has a bright future in pharmaceutical formulation prediction [78, 89].
1.3.2 DL in Dissolution and Release Studies
The poor solubility of APIs poses a significant barrier in dosage form development. Model formulation technology development using DL, especially investigational and predictive tools, is advantageous to solve several difficulties that pharmaceutical manufacturers face. Computational approaches performed by Mendyk et al. (2019) to predict bicalutamide dissolution from solid dispersion formulated using different carriers [90]. AI techniques like decision trees, ANN, DNN, evolutionary computations, and rule-based systems were utilized for dissolution and release studies, which select all fundamental variables by default for successful pharmaceutical formulation. In silico simulations based on ab initio modeling were piloted to expose excipients and drug interactions. Three ML methods, including ensemble of regression trees [ERT], ANN, SVM, were applied to forecast the in vitro dissolution data of sustained-release formulations, among which ANN produced the exact results. In addition, the release rate of the drug from the formulation is influenced by the nature of API, matrix polymer content, polymer particle size distribution in a matrix [PSD], and compression force and all these parameters can be easily evaluated by ML-based techniques [91].
ANN is used as a suitable predicting model for the design of solid dosage formulation and also for evaluating the impact of numerous features such as compression parameters, physicochemical properties, etc. With the application of Chem software, the ANN model gets upgraded based on the input [hardness, particle size, and moisture] and output [percentage of drug release, mechanism of release pattern] data units. Based on optimal in-vitro disintegration time, as well as in vivo release profiles, trained ANN model is utilized to predict the most effective tablet compositions for effective pharmacokinetic action. Combination of fuzzy logic with neural networks provides an effective tool, which offers powerful and flexible results. Model drugs viz. naproxen, carbamazepine, chlorpropamide, ketoprofen, diazepam, and ibuprofen were formulated and subjected to their dissolution performance using Expert Network and the results suggested poor prediction with error. This led to the development of new data with an intelligent hybrid, which was found to be appropriate for the investigation of multiple BCS class II drugs [92] OXPIRT was used to produce an immediate-release generic tablet in pilot-scale production [93]. Commercially available software used a four-layered artificial neural network [4LNN] to forecast the disintegration data from physicochemical characteristics of drugs. The outcomes demonstrated that the 4LNN approach is a superior model for predicting dissolution data when compared to traditional three-layered models [94].
1.3.3 DL in the Manufacturing Process
Following the discovery of a novel therapeutic molecule, the inclusion of these drug molecules into a suitable dosage form with desirable delivery characteristics is a technological art. In this case, AI can take the role of the classic trial-and-error method. Stability, dissolution, porosity, and other formulation design apprehensions can be solved using a variety of computational techniques for the successful production of various pharmaceutical formulations.
As manufacturing processes become more complex and there is a greater desire for effectiveness and superior product quality, advanced manufacturing techniques attempt to deliver human expertise to machineries, which is continually changing industrial practice. AI integration in industrial processes, especially product production, could benefit the pharmaceutical business. Reynolds-Averaged Navier-Stokes solvers expertise has been used to investigate the effect of agitation and stress levels in various machinery, allowing many pharmaceutical activities to be automated. Advanced ways to resolve problems in the pharmaceutical manufacturing process are used in related systems, like large eddy and direct numerical simulations [95].
Discrete element modeling has been widely used in the pharmaceutical manufacturing process, particularly for isolation of powders in a binary mixture, predicting tablets coating techniques, the effects of varying blade speed and shape, and analyzing time consumed by tablets in the spray region. To reduce tablet capping, ANNs and fuzzy tools are being used to investigate the association between machine characteristics with capping concerns on the manufacturing line. Meta classifiers and tablet classifiers are AI technologies that contribute to regulating the final product’s quality standard. AI can also be used to regulate in-line manufacturing processes to attain anticipated product quality. In the total quality management (TQM) skilled method, deep data mining and diverse knowledge discovery protocols can be employed to formulate complicated judgments, including developing new methods for pharmaceutical quality control [88, 95].
DL-based decision-support techniques utilize rule-based systems to choose the type, nature, and quantity of excipients for manufacturing process depending on the drugs’ physical and chemical characteristics. They use a response tool to continuously observe and change the procedure [57, 95]. Guo et al. combined Expert Systems [ES] and ANN to establish a hybrid system to advance direct-filling hard gelatin capsules containing piroxicam that successfully meet dissolving profile parameters. Based on the input parameters, MODEL EXPERT SYSTEM [MES] prepares formulation development recommendations, as well as judgments [95, 96].
Continuous Manufacturing [CM] of pharmaceutical formulation is a novel method in the pharma sector. To examine the process and characterize the impact on quality characteristics, DL techniques are utilized to forecast the quality [output] with numerous critical process parameters [input]. DL reduces noise and simplifies data interpretation for proper understanding of the process. With 2500 epochs, the Rectified Linear Unit [ReLU] activation function and ADAM optimizer have been utilized [number of learning cycles]. With less calibration error [10%], API concentrations, polydispersity index values, and loss on drying values were estimated. The amount of inaccuracy allows DNN to monitor the process effectively, and the most important process parameters may be determined at a complex level of process comprehension [97]. The synergy between process analytical technology and data science creates a superior monitoring framework of the continuous manufacturing line. This raises awareness of this cutting-edge production process, as well as the advancement of AI/ ML/DL in pharmaceutical formulation design and is depicted in Figure 1.2.
A hot-melt extrusion [HME], an ML model created by the utilization of ANN, and decision trees can successfully manufacture the drug products. The role of ANNs in preformulation studies helps to identify physicochemical characteristics of polymers of amorphous nature, glass transition temperature, and flow characteristics [98]. It is also utilized to see how excipient amount, as well as process restrictions, affects medication [prednisolone] release from pellets. ANNs are also applied to evaluate the process parameters of HME for a vaginal film [99]. In all these cases, it has a 1% error for the predicted values compared to experimental data. The application of ANNs for the improvement of [BCS] class IV drugs properties is another breakthrough. ANNs have meritorious applications for ranitidine hydrochloride polymorphic form quantification in multicomponent tablets [100].
Figure 1.2 Applications of AI/ML/DL in the design of pharmaceutical formulations.
3D printing [3DP] is a new production method for accurate personalized drug-loaded formulation production. The application of DL in 3D technology has the advantage to reduce costs and rationalize the formulation parameters of drug-loaded products. In an ML model, generative adversarial networks [GANs] can generate novel chemical structures similar to a drug. It is utilized to enhance film-forming formulations and the DNNs could accomplish prognostic precision, with f2 score of 99.99 [101]. A software, namely M3DISEEN based on ML technique, was applied on a data set containing 145 exclusive excipients with 614 drug-loaded formulations. Supervised ML techniques, like DL, k-nearest neighbors, multivariate linear regression, SVM, traditional neural networks, and random forest, were utilized in rationalizing the pharmaceutical 3DP procedure [102]. Even though the biopharmaceutical industry is hesitant to adopt ML as a standard tool for bioprocess development due to the potentially catastrophic consequences of faulty products, biopharmaceutical manufacturing continues to surprise us with new applications and case studies every year. Technological advancements in DL and computing will inevitably lead to greater use of these techniques, and case studies like this are critical in giving meaningful benchmarking material to the community. In terms of quality control, root cause analysis, predictive maintenance, and waste reduction, as well as optimizing the automation process, DL can provide a greater variety of benefits. This technology appears to work best when combined with standard algorithms to improve process performance and reduce waste and expenses of the pharmaceutical industry.
