Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5443_Библиотеки_им_академика_М_И_Перельмана
.pdf
the excipient with blends at its highest amount, then this implies that compatibility
between both materials is obvious. The score plot of theophylline with MCC and sorbi-
tol are shown in Figure 14.10(a) and (b), respectively. According to these score plots,
theophylline is considered compatible with MCC, while is proven incompatible with
sorbitol.
Figure 14.10: PCA scatter plot for DSC data: theophylline (Th), microcrystalline cellulose (MC), and their
mixtures at ratios 9:1, 7:3, 1:1, 3:7, 1:9 (modified from Khajavi [30] under the terms of the Creative
Commons Attribution License).
14 Role of principal component analysis in drug formulation and delivery 343
https://t.me/med1917
References
[1] Hathout, R. M. Chapter 13 – Machine learning methods in drug delivery. In: Ren, J., Shen, W., Man,
Y., & Dong, L. (eds) Applications of Artificial Intelligence in Process Systems Engineering. 2021,
(pp. 361–380). Elsevier.
[2] Hathout, R. M., Mahmoud, O. A., Ali, D. S., Mamdouh, M., & Metwally, A. A. Modeling drugs-PLGA
nanoparticles interactions using Gaussian processes: Pharmaceutics informatics approach. Journal
of Cluster Science, 33, 2031–2036 (2022); 2021.
[3] Hathout, R. M., Metwally, A. A., Woodman, T. J., & Hardy, J. G. Prediction of drug loading in the
Gelatin Matrix using computational methods. ACS Omega, 2020, 5, 1549–1556.
[4] Hathout, R. M., & Metwally, A. A. Pharmaceutics Informatics: Bio/Chemoinformatics in Drug
Delivery. In: Saharan, V. A. (ed) Computer Aided Pharmaceutics and Drug Delivery: An Application
Guide for Students and Researchers of Pharmaceutical Sciences. 2022, (pp. 705–724). Springer
Nature Singapore: Singapore.
[5] Hathout, R. M. Using principal component analysis in studying the transdermal delivery of a
lipophilic drug from soft nano-colloidal carriers to develop a quantitative composition effect
permeability relationship. Pharmaceutical Development and Technology, 2014, 19, 598–604.
[6] Metwally, A. A., & Hathout, R. M. Computer-Assisted drug formulation design: Novel approach in
drug delivery. Molecular Pharmaceutics, 2015, 12, 2800–2810.
[7] Abd-algaleel, S. A., Metwally, A. A., Bdel-bar, H. M., Kassem, D. H., & Hathout, R. M. Synchronizing In
Silico, In Vitro, and In Vivo studies for the successful nose to brain delivery of an anticancer
molecule. Molecular Pharmaceutics, 2021, 18, 3763–3776.
[8] Pearson, K. LIII. On lines and planes of closest fit to systems of points in space. The London,
Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 1901, 2, 559–572.
[9] Bellman, R., & Kalaba, R. On adaptive control processes. IRE Transactions on Automatic Control,
1959, 4, 1–9.
[10] Soofi, E. S. Capturing the Intangible Concept of Information. Journal of the American Statistical
Association, 1994, 89, 1243–1254.
[11] Bro, R., & Smilde, A. K. Principal component analysis. Analytical Methods, 2014, 6, 2812–2831.
[12] A. Giuliani, The application of principal component analysis to drug discovery and biomedical data,
Drug Discovery Today, 22 (2017) 1069–1076.
[13] Martins, S., Tho, I., Souto, E., Ferreira, D., & Brandl, M. Multivariate design for the evaluation of lipid
and surfactant composition effect for optimisation of lipid nanoparticles. European Journal of
Pharmaceutical Sciences, 2012, 45, 613–623.
[14] Hathout, R. M., El-Ahmady, S. H., & Metwally, A. A. Curcumin or bisdemethoxycurcumin for nose-to-
brain treatment of Alzheimer disease? A bio/chemo-informatics case study. Natural Product
Research, 2018, 32, 2873–2881.
[15] Hathout, R. M., & Woodman, T. J. Applications of NMR in the characterization of pharmaceutical
microemulsions. Journal of Controlled Release, 2012, 161, 62–72.
[16] Singh, S. K., Verma, P. R., & Razdan, B. Development and characterization of a lovastatin-loaded self-
microemulsifying drug delivery system. Pharmaceutical Development and Technology, 2010, 15,
469–483.
[17] Singh, S. K., Verma, P. R., & Razdan, B. Glibenclamide-loaded self-nanoemulsifying drug delivery
system: Development and characterization. Drug Development and Industrial Pharmacy, 2010, 36,
933–945.
[18] Fagir, W., Hathout, R. M., Sammour, O. A., & ElShafeey, A. H. Self-microemulsifying systems of
Finasteride with enhanced oral bioavailability: Multivariate statistical evaluation, characterization,
spray-drying and in vivo studies in human volunteers. Nanomedicine (Lond), 2015, 10, 3373–3389.
344 Rania M. Hathout
https://t.me/med1917
[19] Fagir, W., Hathout, R. M., Sammour, O. A., & ElShafeey, A. H. Self-microemulsifying systems of
Finasteride with enhanced oral bioavailability: Multivariate statistical evaluation, characterization,
spray-drying and in vivo studies in human volunteers. Nanomedicine (Lond), 2015, 10, 3373–3389.
[20] Hathout, R. M., & Omran, M. K. Gelatin-based particulate systems in ocular drug delivery.
Pharmaceutical Development and Technology, 2016, 21, 379–386.
[21] Gad, H. A., & Hathout, R. M. Can the docking experiments select the optimum natural bio-
macromolecule for doxorubicin delivery?. Journal of Cluster Science, 32, 1747–1751 (2021); 2020.
[22] Karthikeyan, S., Rajendra Prasad, N., Ganamani, A., & Balamurugan, E. Anticancer activity of
resveratrol-loaded gelatin nanoparticles on NCI-H460 non-small cell lung cancer cells. Biomedicine
and Preventive Nutrition, 2013, 3, 64–73.
[23] Hathout, R. M., Abdelhamid, S. G., & Metwally, A. A. Chloroquine and hydroxychloroquine for
combating COVID-19: Investigating efficacy and hypothesizing new formulations using Bio/
chemoinformatics tools. Informatics in Medicine Unlocked, 2020, 21, 100446.
[24] Hathout, R. M., & Metwally, A. A. Gelatin Nanoparticles. Methods in Molecular Biology, 2019, 2000,
71–78.
[25] Shokry, M., Hathout, R. M., & Mansour, S. Exploring gelatin nanoparticles as novel nanocarriers for
Timolol maleate: Augmented in-vivo efficacy and safe histological profile. International Journal of
Pharmaceutical, 2018, 545, 229–239.
[26] Abdelrady, H., Hathout, R. M., Osman, R., Saleem, I., & Mortada, N. D. Exploiting gelatin
nanocarriers in the pulmonary delivery of methotrexate for lung cancer therapy. European Journal
of Pharmaceutical Sciences, 2019, 133, 115–126.
[27] Hill, M., Cunningham, R. N., Hathout, R. M., Johnston, C., Hardy, J. G., & Migaud, M. E. Formulation of
antimicrobial tobramycin loaded PLGA nanoparticles via complexation with AOT. Journal of
Functional Biomaterials, 2019, 10(2), 26.
[28] Saraogi, G. K., Sharma, B., Joshi, B., Gupta, P., Gupta, U. D., Jain, N. K., & Agrawal,
G. P. Mannosylated gelatin nanoparticles bearing isoniazid for effective management of
tuberculosis. Journal of Drug Targeting, 2011, 19, 219–227.
[29] Naidu, B. V. K., & Paulson, A. T. A new method for the preparation of gelatin nanoparticles:
Encapsulation and drug release characteristics. Journal of Applied Polymer Science, 2011, 121,
3495–3500.
[30] Khajavi, F. Principal component analysis in drug-excipient interactions. Acta Scientific Reports, 2022,
3, 14–19.
14 Role of principal component analysis in drug formulation and delivery 345
https://t.me/med1917
https://t.me/med1917
Vimal Arora, Payal Mittal, and Sanjay Kumar Elisetti
15 Computational approaches for predicting
drug solubility and permeability
in pharmaceutical formulation
Abstract: This study explores pharmacokinetic interactions among drugs, elucidating
instances where one drug influences the absorption, transportation, distribution, me-
tabolism, or excretion of another within the body. The development of pharmaceuti-
cal drugs is a complex, multidisciplinary endeavor, requiring a deep understanding of
factors influencing drug efficacy and bioavailability, with a focus on the impact of
chemical and physical properties on drug solubility and permeability. The likelihood
of drug interactions increases for highly soluble drugs, manifesting through competi-
tive binding to transporters or enzymes, or by modifying the solubility of coadminis-
tered drugs. Targeted drug delivery systems play a vital role in enhancing drug
permeability and targeting, particularly in addressing central nervous system disor-
ders, where drugs must possess specific physical properties and emp loy targeted
transport mechanisms or strategies to cross the blood–brain barrier. Physical proper-
ties also wield influence over drug clearance and metabolism, further underscoring
their significance in pharmaceutical development. The integration of computational
approaches, specifically those leveraging artificial intelligence, has emerged as a
transformative force in predicting drug solubility and permeability, gaining substan-
tial popularity in drug discovery and development.
In the realm of drug development, drug solubility and permeability stand as essen-
tial factors directly shaping a drug candidate’s success or failure. This abstract provides
a comprehensive overview of the intricate dynamics of pharmacokinetic interactions,
the impact of chemical and physical properties, targeted drug delivery, and the transfor-
mative role of computational approaches in pharmaceutical research and development.
Keywords: Artificial Intelligence (AI), Bioavailability, Blood Brain Barrier (BBB), Per-
meability, Machine Learning (ML), Prodrug, Pharmacokinetics, Transdermal Drug De-
livery, Targeted Delivery, Computational Model, Deep Learning, Transfer Learning,
Biological Barrier
Vimal Arora, University Institute of Pharma Sciences, Chandigarh University, Gharuan, Mohali, Punjab
(INDIA) draroravimal@gmail.com
Payal Mittal, Sanjay Kumar Elisetti, University Institute of Pharma Sciences, Chandigarh University,
Gharuan, Mohali, Punjab, India
https://doi.org/10.1515/9783111208671-015
https://t.me/med1917
15.1 Introduction
The pharmaceutical industry is continuously working to provide an affordable health-
care solution aiming to design innovative pharmaceutical products with better efficacy.
The prevailing pharmaceutical drug development process still relies steadily on tradi-
tional trial and test appro ach, consuming time, resources, manpower, etc., and thus
involving higher financial involvement. Furthermore, it is difficult to achieve the opti-
mum formulations conveniently by hit-and-trial studies and it requires exhaustive data
handling. Therefore, the simplification of the formulation development process be-
comes essential for formulation scientists, especially those working in the domain of
designing bioequivalent products [1].
Moreover, R&D and regulatory approval of a drug are estimated to take about
11–15 years on an average, with a budget of about US $2.5 billion, approximately [2],
which does not assure qualification or successful commercialization of a drug product
[3]. In a product formulation design, a keen knowledge of processes and interaction of
the components is key to understand the intricate relationship between the ingredients
of a formulation [4]. To achieve such knowledge, proper tools are needed that can
make the laboratory trials easier and optimize the goals efficiently. Utilizing appropri-
ate tools can maximize the success rate of the formulation design by rectifying the pre-
viously encountered defects (gaps) in the process. These tools can be used efficiently to
enhance the quality in both the development stage and marketable manufacturing [5].
Research centers and pharmaceutical industries are evolving with such methods to un-
derstand the processes in a much easier way using tools and software [6]. The develop-
ment of these tools has offered them an advantage in avoiding expensive and time-
consuming laboratory trials, thus leading to a cost-effective product design process.
With the increasing need and advancements in industrial procedures, there is a
need for a better outcome in the process of dosage form design, which can be made
possible with the incorporation of artificial (AI). AI refers to the creation of computer
system solutions that simulate intelligence. It has brought about advancements in fields,
including speech and image recognition, market analysis, and even the realm of drug
design, within the pharmaceutical industry. There is a definite need to explore this field
and its application in drug product design. It is predicted by Fior Markets that AI will
escalate the global drug discovery market to US $40.36 billion by 2027 [7, 8].
In recent years, pharmaceutical industries have seen a significant increase in
data digitization; however, the burden of obtaining, assessing, and using data to solve
complex problems such as clinical data, analytical data, and other regulatory informa-
tion has also increased [9, 10].
This supports the use of AI in the pharmaceutical industry as it is capable of han-
dling huge data with advanced computerization, algorithms, and data networking,
which provides certain decisive outcomes [11]. An important advantage of such sys-
tems is that they can easily overcome all the limitations of the traditional approach to
pharmaceutical design without replacing humans. The healthcare industry is nowa-
348 Vimal Arora, Payal Mittal, and Sanjay Kumar Elisetti
https://t.me/med1917
days getting fully automated and concise enough to mimic the decisive power of the
human brain with the incorporation of AI as represented in Fig 15.1 [12]. This remark-
able development is also supported by machine learning (ML) [13], which utilizes sta-
tistical methodologies to learn with or without being explicitly programmed to make
certain decisions based on previously established experime ntal data [10, 11, 14]. ML
has been divided into three categories: supervised, unsupervised, and reinforced
learning. Classification and regression procedures are used in supervised learning,
and the predictive model is built using input data and output sources, which is re-
ported to be effectively used in the field of disease diagnosis under the classification
method; and predictive drug efficacy as well as absorption, distribution, metabolism,
and excretion (ADME) profiling under the regression subgroup [15]. On the other side,
unsupervised learning consists of grouping and feature-finding methodology via group-
ing, sorting, and evaluating the data based on input data or experimental data, which is
used in the subgrouping [16]. It can help in subgrouping and classification of diseased
states [16,17]. The third category, known as reinforcement learning, is solely known for
its decision-making capability, leading to the maximization of performance, and it helps
in making decisions based on the training data or input dataset (experimental/existing
data) [18]. Thus, the outcomes of AI and ML can be effectively used for de novo drug
design and experimentation through quant um chemistry and modeling techniques
[19, 20].
Figure 15.1: Applications of AI.
15 Computational approaches for predicting drug solubility 349
https://t.me/med1917
Another class of ML named deep learning (DL) utilizes the concepts of artificial
neural networks to interpret massive experimental data [21, 22]. Therefore, it may be
stated that the big data associated with data mining algorithms can offer the ability to
explore new chemical entities for their potential to be a successful new drug, and also
help in studying as well as proving the synergy between two or more drugs and in
exploring new moieties. Thus, it can help in reinventing new healthcare solutions in
terms of precision medicines and exploring new moieties [23]. The key feature of DL
that makes it different from other ML techniques is its flexible architecture of neural
networking, namely recurrent neural networks, convolutional neural networks, and
feed-forward networks, which helps in multidirectional analysis of the given dataset
[19, 24]. Therefore, it has been predicted that these advancements in the field of AI
and ML-assisted technologies can lead to a remarkable paradigm shift in the field of
healthcare solutions with minimized risk of failures during clinical trials, with the
extra advantage of being faster, economical, and effective [25–27].
Pharmaceutical preparations can be categorized into various dosage forms like solid,
semisolid, liquid, etc., which can be studied for their various properties with the help of
AI [28]. Here, by using AI, we can test the hardness, disintegration time, brittleness, and
coating thickness, along with drug–drug , drug–excipients, and excipient–excipient inter-
actions [29]. In certain cases, pharmacokinetic interactions between different drugs af-
fects the extent of absorption, transportation, distribution, metabolism, or excretion of
another drug. These interactions can lead to changes in drug concentration in the body
(bioavailability), which can have an impact on the effectiveness and safety of medications
for patients. On the other hand, the properties of excipients used in the drug product
design (whether they are naturally derived or synthetic) can also affect the efficacy and
performance of the drug product. Excipients can vary in their characteristics due to dif-
ferences in their source of origin and manufacturing processes. For example, microcrys-
talline cellulose is an excipient derived from wood pulp that may require batch blending
to meet product requirements due to variations in its natural source. Excipients with var-
iability can potentially impact the potency and purity of a drug product well as affect the
performance and quality of delivery systems like modified-release formulations. It is im-
portant to note that among suppliers and batches, there may be variations in the quality
of excipients used. Pharmaceutical product development follows the basic foundation
process and monitors different interactions where excipients have been given due con-
sideration in the process of product improvement [30]. This conventional approach of
formulation design is more time-consuming, along with a good deal of use of materials,
human resources, and money. By the application of AI, we can optimize the consumption
of these resources and hence assure efficient productivity.
350 Vimal Arora, Payal Mittal, and Sanjay Kumar Elisetti
https://t.me/med1917
15.2 Impact of chemical and physical properties
on drug solubility and permeability
in pharmaceutical products
The development of pharmaceutical drugs is a complex and multidisciplinary field that
requires a deep understanding of various factors influencing drug efficacy and bioavail-
ability. Among these factors, the solubility and permeability of a drug are critical determi-
nants of its pharmacokinetic profile. Solubility refers to the ability of a drug to dissolve in
a particular solvent, typically water, while permeability relates to the ability of a drug to
pass through biological barriers, primarily cell membranes, which directly impacts the
extent of drug availability at a particular site. Both properties are influenced by a range
of chemical and physical characteristics of the drug molecule. It is very interesting to ex-
plore how these properties are affected by various factors, with a focus on their implica-
tions for pharmaceutical product development and therapeutic efficacy [31].
Pharmaceutical products encompass a wide range of drugs and therapeutic agents
designed for patient use for treating various ailments. The chemical properties of these
drugs play a crucial role in determining their solubility, which in turn, influences their
formulation design and efficacy:
(1) Product formulation design: Solubility is a critical consideration in product formu-
lation. Pharmaceutical scientists have to make sure that the active pharmaceutical
ingredient should be dissolved in a suitable solvent or mixed with appropriate ex-
cipients to create a dosage form that patients can administer for better compliance.
For example, in the case of highly lipophilic drugs, solubilization techniques such
as the use of surfactants or cosolvents are being used to create a stable and bio-
available drug formulation [32].
(2) Bioavailability: The solubility of a drug directly affects its bioavailability, which is
the fraction of the administered dose that reaches the systemic circulation. Drugs
with poor solubility may have lower bioavailability, leading to suboptimal thera-
peutic outcomes. Therefore, understanding and optimizing solubility is crucial for
ensuring that pharmaceutical products achieve the desired therapeutic effect [33].
(3) Dosing regimen: The solubility of a drug can also impact the dosing regimen for
pharmaceutical products. Drugs with high solubility may be formulated as lower-
dose or more concentrated formulations, while poorly soluble drugs may require
higher doses or extended-release formulations to maintain therapeutic levels in
the body over time [34].
(4) Drug–drug interactions: The solubility of a drug can influence its potential for
drug–drug interactions. Highly soluble drugs may have a greater likelihood of in-
teracting with other drugs in the body, either through competitive binding to trans-
porters or enzymes or by altering the solubility of coadministered drugs [35].
(5) Salt formation and prodrugs: As mentioned earlier, the conversion of a drug into
a salt form can significantly improve its solubility. This strategy is commonly em-
15 Computational approaches for predicting drug solubility 351
https://t.me/med1917
ployed in pharmaceutical development to enhance the solubility of poorly soluble
drugs. Additionally, prodrugs, which are biologically inactive compounds that
convert into active drugs in the body, are designed to improve solubility and per-
meability, among other properties [36].
The physical properties of drugs, including their size, shape, and lipophilicity, have a
profound impact on their permeability through biological barriers. These properties
influence drug absorption and distribution, and ultimately, therapeutic efficacy [37]:
(1) Oral drug delivery: T he majority of pharmaceutical products are administered
orally. For orally administered drugs, the permeability of the drug molecule
through the gastrointestinal tract is crucial. Lipophilic drugs are often better ab-
sorbed from the gastrointestin al tract because they can pass through the lipid-
rich cell membranes of the intestinal epithelium [38].
(2) Transdermal and topical formulations: For transdermal and topical pharmaceuti-
cal products, the physical properties of the drug are critical for skin penetration.
Smaller, lipophilic molecules are more likely to penetrate the stratum corneum,
the outermost layer of the skin and reach the systemic circulation. Conversely,
larger or more hydrophilic molecules may have limited skin permeability and
may require formulation strategies to enhance their absorption [39].
(3) Intravenous and parenteral administration: In contrast to oral administration, in-
travenous and parenteral routes bypass many barriers to drug absorption. How-
ever, the physical properties of the drug can still affect its distribution and
clearance within the body. Lipophilic drugs may have a higher volume of distri-
bution, leading to a longer duration of action, while hydrophilic drugs may have
a shorter half-life [40].
(4) Targeted drug delivery: Pharmaceutical products often employ drug delivery sys-
tems designed to improve drug permeability and targeting. Liposomal and nano-
particle-based drug delivery systems c an encapsulate drugs, altering their
physical properties to enhance permeability, prolong circulation time, and target
specific tissues or cells [41].
(5) Biological barriers: The physical properties of drugs are crucial when it comes to
crossing biological barriers such as the blood–brain barrier (BBB) or the placental
barrier. The BBB restricts the passage of many drugs into the brain due to its
unique endothelial cell structure. To be effective against central nervous system
disorders, drugs must have both the appropriate physical properties and specific
transport mechanisms or strategies to cross the BBB [42].
(6) Drug clearance and metabolism: Physical p ropert ies also influence drug clear-
ance and metabolism. Small, lipophilic molecules are often more readily elimi -
nated by the liver and kidneys, whereas larger or more polar molecules may
undergo slower clearance. The physical properties of drugs can affect their sus-
ceptibility to metabolism by enzymes in the liver or other tissues [43].
352 Vimal Arora, Payal Mittal, and Sanjay Kumar Elisetti
https://t.me/med1917
Соседние файлы в папке Библиотека им академика М.И. Перельмана