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Pharmaceutical Dosage Forms and Drug Delivery
expensive process, with preclinical stages taking three to six years and costing hundreds of millions to
billions of dollars. Newer techniques like AI tools are revolutionizing every stage, potentially reshaping
the drug development speed and economics. Integrating AI into medication development processes can
can help mainly in the:
1.5.1.1 Target Identification
methods rely on cell culture and animal models, which can be time- consuming and costly, even when
using high throughput screening. AI is being trained on massive datasets, such as omics datasets, phenotypic and expression data, disease associations, patents, publications, clinical trials, research grants, and
more, in order to understand the fundamental mechanisms of diseases and to identify novel proteins
or genes that can be targeted to treat those diseases. Further, when integrated with systems such as
using knowledge graphs (KGs). These graph- structured databases store interlinked descriptions of nodes
and encode underlying relationships. KG data projection enables network- based analytical algorithms,
1.5.1.2 Molecular Simulations
Molecular simulations, which imitate particle movements based on fundamental physical rules, are
putation of coordinate derivatives or forces associated with an energy function, despite the fact that
numerous molecular simulation packages, including CHARMM, AMBER, GROMACS, NAMD,
LAMMPS, OpenMM, and SPONGE, are already available. These are complicated specialized codebases
written in programming languages, which hinders their ability to adapt to the constantly evolving sci-
graphics processing units (GPUs). Traditional molecular dynamic (MD) tools are no longer capable of
processing innovative concepts that incorporate deep learning, such as AlphaFold, Boltzmann Generator,
and machine- learned potentials, which are accessible through AI.
AI is utilized to minimize the necessity for physical testing of potential medicinal compounds by
sive nature of conventional chemical techniques. A novel molecular generator called Structure- Based
de Novo Molecular Generator (SBMolGen) has been developed lately. It combines a recurrent neural
network, a Monte Carlo tree search, and docking simulations. The molecules created by it showed a
including two kinases and two G protein- coupled receptors. The molecules also had a wider chemical
space distribution.
1.5.1.3 Prediction of Drug Properties
Certain AI systems are being employed to circumvent the need for simulated drug candidate testing
through the prediction of critical properties, including physicochemical characteristics, bioactivity, and
toxicity. AI can play a crucial role in identifying drug- drug interactions, where multiple drugs are combined
for the same or different diseases in the same patient, causing altered effects or adverse reactions. This
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This allows for the customization of therapies to meet the requirements of individual patients. Using this
them to 497 compounds to predict their intestinal absorptivity. These models were based on parameters
such as molecular surface area, mass, hydrogen count, refractivity, volume, logP, total polar surface area,
E- state indices, solubility index, and rotatable bonds.
1.5.1.4 De- Novo Drug Design
AI is also changing the conventional approach to drug discovery, which previously comprised the evaluation of extensive collections of candidate molecules through screening. Certain AI systems possess the
ability to generate novel and potentially effective drug molecules from scratch. For example, a groundbreaking software platform called AlphaFold uses protein sequence data and AI to predict protein threedimensional structures. This breakthrough in structural biology is expected to revolutionize personalized
medicine and drug discovery. Generative chemistry uses current AI- based generative modeling tools to
make compounds that are easy to synthesize and have drug- like properties while also meeting the desired
target property description. AI- driven generative modeling tools excel in generating, predicting, and
selecting substances with desirable attributes. Various deep learning architectures, including Variational
Autoencoders (VAEs), Generative Adversarial Networks (GANs), Reinforcement Learning (RL), and
Recurrent Neural Networks (RNNs), have been utilized for creating new molecular structures. A drug
concept to novel target discovery and preclinical drug candidate selection, took under 18 months and cost
a fraction of a conventional preclinical program.
1.5.1.5 Candidate Drug Prioritization
After identifying a group of potential “lead” therapeutic compounds, AI is utilized to prioritize and rank
these molecules for future evaluation, surpassing traditional ranking methods. Many tools, including
accessible and utilize convolutional neural network (CNN), deep neural network, and RF principles.
These tools enable the prediction of physicochemical properties and toxicity of compounds from diverse
compound libraries. Pharmaceutical scientists working with AI can enhance the precision and effectiveness of clinical trials by utilizing AI algorithms to examine trial data for patterns and probable medication side effects. The pharmaceutical industry may use this to make educated judgments on which drug
candidates to pursue, perhaps accelerating the drug development process. Researchers trained a neural
network on 2,335 unique compounds to identify molecules that inhibit E. coli growth. The model was
promising compound, c- Jun N- terminal kinase inhibitor SU3327, was found to be deadly against E. coli.
1.5.1.6 Synthesis Pathway Generation
AI is utilized not just for theoretical drug design but also for creating synthesis routes to produce hypothetical pharmacological compounds. It may even propose alterations to compounds to simplify their
manufacturing process. Recently, researchers used an AI- driven robotic chemist called SynBot, which
is capable of executing tasks ranging from synthetic planning to batch reactor experiments. The Synbot
consists of three layers: an AI software layer, a robot S/ W layer, and a robot layer, with the AI S/ W layer
leading the synthesis planning process, retrosynthesis, DoE, optimization, and decision- making modules.
1.5.1.7 Assistance in Disease Diagnosis
tology. Digital pathology digitizes histopathology, immunohistochemistry, and cytology slides, training
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Pharmaceutical Dosage Forms and Drug Delivery
AI systems on this data. Computational pathology aims to reduce diagnostic errors and accelerate biomarker discovery. AI can improve radiology diagnoses by analyzing chest X- rays, magnetic resonance
images, and computed tomography scans. An ML model based on convolutional neural networks has
demonstrated near- physician accuracy in diagnosing atopic dermatitis and distinguishing it from other
skin conditions.
1.5.2 AI Techniques
Markov decision process, and Natural language processing (NLP).
1.5.2.1 Heuristics
This method seeks a satisfactory answer from the options at hand without requiring it to be perfect.
This semi- exhaustive approach aims to minimize the time needed for reconstruction, enabling the quick
development of high- quality new ligands. This approach can help to identify lead molecules from the
compound library.
1.5.2.2 Support Vector Machines (SVM)
-
egies in the drug development process. Using the available information, this method may effectively
distinguish between active and nonactive compounds in the library.
1.5.2.3 Artificial Neural Networks (ANN)
ANN are computational systems that mimic the neuronal structure of the human brain. It is possible to
arrange the neurons of the ANN in multiple layers. The input layer of this network receives, processes,
and transforms all incoming data into outputs utilized by the subsequent layers. The concealed layers
are composed of one or more neuronal layers that traverse via inputs and outputs. Ultimately, the user’s
the architecture and neuron connectivity patterns, these can be multilayered perceptron, Kohonen neural
networks, counter propagation networks, Bayesian neural networks, and recurrent neural networks.
and identify the response pattern of a disease to a drug.
1.5.2.4 Markov Decision Process (MDP)
It is a paradigm for modeling decision- making in which the outcome is partially dependent on the input of
the decision- maker in certain circumstances and partially dependent on random chance in other scenarios.
The fundamental objective of the MDP is to locate a policy for the decision- maker that outlines the exact
actions that ought to be taken at a particular moment in time during the given situation. For instance, the
MDP model can be applied to decision- making in order to select the most effective treatment for a patient
by taking adverse drug reactions associated with the drug into account.
1.5.2.5 Natural Language Processing (NLP)
NLP involves tasks like speech detection and language production, requiring diverse approaches like
part- of- speech tagging, named entity recognition, and parsing. It aids in mining the relevant literature
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from biochemical text, enhancing biochemical and biological knowledge, and accelerating drug discovery for human health improvement.
1.5.3 Machine Learning in Drug Design and Drug Discovery
Drug discovery traditionally involves labor- intensive, time- consuming experimentation to assess potential effects on the human body. This slow, costly, and uncertain process can be subject to high variability.
AI techniques like machine learning (ML) can overcome these limitations by analyzing large amounts
of information and identifying patterns and trends that may not be apparent to human researchers.
kinase 1 (MEK) protein, a potential cancer treatment target, overcoming challenges in developing
effective inhibitors and highlighting the potential of ML in drug discovery. ML is divided into four categories: Supervised, Unsupervised, Semi- supervised, and Reinforcement learning, each distinguished by
its approach and learning techniques.
1.5.3.1 Supervised Machine Learning
This strategy involves using a labeled dataset to train the machines by providing particular input
parameters that guide the machines in producing the output. This device uses training data to forecast the
result. As an example, a machine can be trained by scientists using data pertaining to a class of established
drug as well.
1.5.3.2 Un- Supervised Machine Learning
This strategy involves training the computer using an unlabeled dataset without supervision. The primary
objective of the unsupervised learning method is to cluster or classify the unorganized dataset based
on similarities, patterns, and distinctions. For instance, preclinical investigations involving a series of
recently synthesized molecules, and clinical studies involving thousands of patients may generate voluminous data devoid of any discernible pattern. In that case, unsupervised ML can make clusters of similar
results and identify outliers to aid in decision- making.
1.5.3.3 Semi- Supervised Machine Learning
Semi- supervised learning utilizes all available data, not only labeled data, as supervised learning. The
training step involves a combination of labeled and unlabeled datasets. It is an intermediary between
supervised (with labeled training data) and unsupervised (with no labeled training data) learning methods.
1.5.3.4 Reinforcement Learning
Reinforcement learning is a method of training machine learning models to make judgments in com-
to problems, with rewards or penalties given to the computer for achieving the programmer’s desired
outcomes. This method can optimize the physicochemical properties of molecules in a certain direction
using existing information.
1.5.4 Deep Learning in Medicine
Deep learning is a branch of machine learning that uses complex structures or nonlinear transformations to abstract data through multiple processing layers. Deep learning neural networks, similar to
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Pharmaceutical Dosage Forms and Drug Delivery
Failure can arise from various factors, including off- target effects, inadequate effectiveness, or
unforeseen adverse effects. Deep learning can predict the biomolecular targets, off- targets, and hazardous byproducts of pharmaceuticals and avoid drug rejection. Recently, a deep learning algorithm
was trained on a dataset of known drug compounds to propose new therapeutic molecules with desir-
design.
1.5.5 Limitations of Utilizing AI for Drug Discovery
Implementing AI in drug discovery faces challenges, including the need for large amounts of high- quality
training data. This data generation is resource- intensive and time- consuming, leading to a compound pro-
-
sion will also be biased. Therefore, increasing the quantity and diversity of data available can improve the
accuracy and dependability of the outcomes.
Also, ML and DL algorithms lack transparency, limiting their utility for decision- making. Preclinical
data rely on proxy measures like cellular target engagement, patient- derived xenograft (PDX) mouse
tumor models, human HepG2 cells, and Caco- 2 cell permeability assays. These data points cannot reliably be used to train AI models to predict clinical outcomes, limiting their utility in drug discovery. To
overcome this, implement explainable AI (XAI) techniques, which seek to present transparent and inter-
and equity in AI- driven decisions.
1.6 Summary and Outlook to the Future
Drug discovery is inherently a complex, multidisciplinary endeavor that requires close collaboration of
highly skilled professionals, integration of each discipline’s output, and relatively long timelines with
different stages of development that have stage gates to check the progression of the compound to later
stages of development and commercialization. This process is also continuously evolving as the drug
targets change, pressures on the biopharmaceutical industry increase, and our understanding of the fundamental mechanisms improve. Teamwork and collaboration form the hallmarks of cultural norms required
in this high- paced environment. In addition, idiosyncratic cross- functional application plays a unique role
in breakthrough drug discovery.
data and trend analysis help in decision- making at all stages of development. Greater ability for clinical
monitoring and preclinical toxicology assessment is enabling the development of safer drugs and early
assessment of potential toxicological liabilities. Increasingly, drugs are being developed in a patientcentric manner to serve the needs of given subpopulations of patients.
Recent trends in drug development also highlight the increasing use of the outsourcing model to
allow investment of scarce internal resources on the highest- value items. Typical modern- day biopharmaceutical companies collaborate with several contract research organizations (CROs), contract
manufacturing organizations (CMOs), and academia alike to execute just about every step of the
drug discovery and development process. Organizations increasingly focus on generating intellectual property and maximizing the speed to market while maintaining and improving product quality
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outcomes. These internal emphases with external forces make the industry a continuously evolving
endeavor.
The integration of AI into drug discovery and development has expedited the expansion of the pharmaceutical sector, resulting in a dramatic shift in the industry’s dynamics. AI could be advantageous at each
stage of the drug discovery pipeline, including generative models for designing new synthetic molecules,
approval processes.
Review Questions
1.1
A Antibodies are hydrophilic
B Antibodies are large molecular- weight compounds
C Antibodies typically have slow off rates on receptors that they occupy
D All of the above
1.2 What is the sequence of activities in the new drug development?
A Preclinical development > clinical development > compound characterization >
commercialization
B Compound characterization > preclinical development > clinical development >
commercialization
C Clinical development > preclinical development > compound characterization >
commercialization
1.3 All drugs intended for human administration must be manufactured and released by utilizing
practices that conform to which of the following? Check any two that apply.
A Good manufacturing practices
B Good clinical practices
C Good laboratory practices
D The International Council on Harmonisation
1.4 Please categorize the number of subjects tested in clinical trials in the increasing order of the
different phases of clinical studies.
A Phase 1 > phase 2 > phase 3
B Phase 1 > phase 3 > phase 2
C Phase 2 > phase 1 > phase 3
D Phase 2 > phase 3 > phase 1
E Phase 3 > phase 2 > phase 1
F Phase 3 > phase 1 > phase 2
1.5 Typical toxicology studies must be carried out in how many different species of animals before
A 1
B 2
C 3
D 4
E 5
Which of the following is not an analytical method used to test the quality of a drug?
A Purity
B Potency
C
D Water content
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Pharmaceutical Dosage Forms and Drug Delivery
1.7
A Developing sophisticated computer games
B Making a machine Intelligent
C Programming on a machine with your own intelligence
D Maximizing use of machine
1.8
A Take over data processing.
B Automating processes to rule out human error.
C Design new drug structures and simulate target integration.
D All of the above.
REFERENCES
Endo A. (2010) A historical perspective on the discovery of statins. Proc Jpn Acad Ser B Phys Biol Sci
86
Nat
Rev Drug Discov 15
Further Readings
Arnold C. (2023) Inside the nascent industry of AI- designed drugs. Nat Med
Blanco- Gonzalez A., Cabezon A., Seco- Gonzalez A., Conde- Torres D., Antelo- Riveiro P., Pineiro A.,
Garcia- Fandino R. (2023) The role of AI in drug discovery: challenges, opportunities, and strategies.
Pharmaceuticals (Basel), 16
Drews J. (2000) Drug discovery: a historical perspective. Science 287
Endo A. (2010) A historical perspective on the discovery of statins. Proc Jpn Acad Ser B Phys Biol Sci
86(5
Annu Rev
Pharmacol Toxicol 64
Hughes J. P., Rees S., Kalindjian S. B., and Philpott K. L. (2011) Principles of early drug discovery. Br J
Pharmacol 162
Narang A. S., and Desai D. S. (2009) Anticancer drug development. In Lu Y., and Mahato R. I. (Eds.)
Pharmaceutical Perspectives of Cancer Therapeutics
AI in drug discovery and its clinical relevance. Heliyon, 9: e17575.
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2
Drug Development
On completion of this chapter, the students should be able to
1. Describe the drug development and regulatory process.
2. Discuss the role of the Food and Drug Administration (FDA) in the approval of a dosage form.
3. Differentiate between an investigational new drug (IND) application, a new drug application
(NDA), and a biologics license application (BLA).
4. Identify the three key components of pharmaceutical development.
5. Identify the objectives and key deliverables of the three stages of clinical trials.
Describe a drug’s life cycle and how it is driven by intellectual property rights.
2.1 Introduction
expensive. A new molecular entity (NME), sometimes also called a new chemical entity (NCE), is
which is followed by extensive animal and human testing. On average, it costs a company more than $1
trials is approved for commercialization.
New drugs include prescription drugs, over- the- counter (OTC) medications, generic drugs, biotechnology products, veterinary products, and/ or medical devices. OTC drugs do not require a physician’s
prescription. Drug development also focuses on new dosage forms, routes of administration, and delivery
or an enzyme whose inhibition may help in a disease state. The structural features necessary in a potenin silico molecular modeling. Several drugs may be synthesized
using combinatorial chemistry and screened for in vitro activity in high- throughput assays. The lead
candidates are then synthesized in larger quantities, screened for biological activity, and further optimized
site is likely to have minimal nontarget effects, which often lead to adverse effects and toxicity related to
for the desired pharmacological effect), minimizes the required dose of a compound, which can reduce
adverse effects and toxicities not associated with the drug’s mechanism of action. Such a drug candidate
Figure 2.1), which enters the development pipeline.
Drug development studies include preclinical studies, whereby a compound is thoroughly characterized
for physicochemical characteristics and is tested in animal models for toxicity and activity. This is
dose- escalating studies, which could be single- dose (called single ascending dose [SAD]) and/ or
LEARNING OBJECTIVES
DOI: 10.1201/9781003389378-3
23
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Pharmaceutical Dosage Forms and Drug Delivery
FIGURE 2.1
species. This is followed by the submission of an investigational new drug (IND) application to the regulatory agency,
such as the U.S. Food and Drug Administration (FDA). The drug then enters clinical trials: Phase I, phase II, and phase
III. Pharmaceutical development proceeds concurrent with clinical development and with the objectives of supporting the
ongoing clinical studies (providing information, documentation, and the drug product for administration to the subjects)
and preparation for commercialization of the product. Following successful clinical and pharmaceutical development, a
required for the commercialization of a new drug product. Several activities on the drug product continue after commercialization, such as adverse event monitoring, development of line extension products, and additional clinical trials to support
label claims or expand target patient populations.
multiple- dose (called multiple ascending dose [MAD]) studies. In these studies, a subject is administered
vious cohort (hence the term ascending dose studies). Increasingly extensive and thorough toxicological,
pharmacological, and pharmacokinetic characterization is carried out in humans during phase II and
phase III clinical trials (Figure 2.2).
Stages of drug development that precede human testing are termed preclinical development, while
the human testing stage of a drug is termed clinical development. The transition from preclinical development to clinical development requires regulatory approval through an investigational new drug
Administration (FDA) with preclinical data and proposed protocol for clinical testing. Unless the FDA
(phase I) clinical studies.
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Drug Development
FIGURE 2.2 Timeline of different phases of drug development. Discovery and preclinical testing to identify a lead compound and its detailed characterization for toxicity and bioactivity in vitro and in vivo can take a few years, such as about
duration of time and number of patients required for higher stages of clinical trials. Agency review of the submitted new
drug application (NDA) can take several more months, depending on prioritization and workload considerations. A typical
medium- to large- sized biopharmaceutical company has several pipeline candidates that are at various stages of development.
25
approval of any new drug product. The clinical evidence is typically gathered in a phased manner,
wherein the toxicity or adverse events of a drug are assessed in an increasing number of subjects as a
molecule progresses through the development timeline (Figure 2.3).
Pharmaceutical development follows a parallel track with preclinical and clinical development.
Pharmaceutical development is responsible for the chemistry, manufacturing, and control (CMC) of both
the drug substance and the drug product throughout the life cycle of a compound. This function provides
a robust dosage form that meets three key requirements of a drug product: (a) stability, (b) bioavailability,
and (c) manufacturability. The key roles of pharmaceutical development are to provide a suitable drug
product in a stage- appropriate manner while also ensuring a path to future development and commercialization and to bridge the drug product used during different stages of development.
2.2 Stage- Gate Process of New Drug Discovery and Development
The new drug discovery and development process can be divided into distinct sequential phases that
evaluate and develop the drug- like characteristics of a potential new compound being considered a drug
candidate (Figure 2.1). The progression of new drug candidates through various stages of this sequential
process depends on the successful demonstration of drug- like characteristics in each of these phases.
Scientists working in a wide array of disciplines are responsible for both the characterization and enablement of drug- like properties in new drug candidates throughout these stages of drug development. These
stages include early discovery, preclinical development, FIH, registrational clinical studies, commercialization, and life cycle management. There is a progressive reduction in the number of molecules that
progress through this stage- gate process, concurrent with increasing knowledge, patient exposure, and
understanding of the molecule (Figure 2.3).
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