Introductory Chapter: The Modern-Day Drug Discovery

Full text

Selection of our books indexed in the Book Citation Index
in Web of Science™ Core Collection (BKCI)
Interested in publishing with us?
Contact book.department@intechopen.com
Numbers displayed above are based on latest data collected.
For more information visit www.intechopen.com
Open access books available
Countries delivered to Contributors from top 500 universities
International authors and editor s
Our authors are among the
most cited scientists
Downloads
We are IntechOpen,the world’s leading publisher ofOpen Access booksBuilt by scientists, for scientists
12.2%
120,000
135M
TOP 1%
154
4,600


Chapter
Introductory Chapter: The
Modern-Day Drug Discovery
ParthaKarmakar, AshitTrivedi and VishwanathGaitonde
. Drug discovery: a brief outline of  years of history
The history of drug discovery and development is as old as some of the oldest
human civilizations. The practice of Ayurveda in India and traditional Chinese
medicines in China are over 5000-year-old therapeutic traditions that are still
in practice at large. Papyrus Ebers is evidence of medicinal practice in Egypt
about 3000 years ago [1–6]. The Greek and Roman medicines became popular
in Europe and western Asia between ~700 BC and 200 BC [7]. The ancient Arab
medicines were in practice to a great extent until 1500 AD and are still in use in the
Mediterranean gulf [8, 9]. The beginning of modern era medicine can be consid-
ered from the time when Edward Jenner discovered immunization for smallpox.
The development in the field was gradual until Sir Alexander Fleming discovered
Penicillin in 1928; since then, the field of medicinal chemistry and drug discovery
has flourished, and by the end of the twentieth century, it became a complex inter-
disciplinary platform primarily based on synthetic organic chemistry expanding
into various biological specificities [10–13]. As a result, the global pharmaceutical
market strengthened to nearly 400 billion US dollars by the year 2001 [14, 15].
. Modern-day fabric of pharmaceutical industry
At the beginning of the twenty-first century, drug discovery research faced
new challenges transforming the classical concept of drug development that was in
practice for half a century. With advances in science and technology, the pharma-
ceutical, health care, and IT industry, accompanied by high-pace shifts in the global
economy, bolstered the process of modern-day drug discovery and development to
a large significance. Novel interdisciplinary research involving metal and polymer
nanoparticles, liposomes, antibodies, and neo-antibiotics in both academia and
industries have opened venues for precision diagnosis, targeted drug delivery,
and innovative immunotherapy [16–24]. Although the classical steps in drug
discovery (involving target validation, lead molecule design, chemical synthesis,
pre-clinical evaluation, ADME, clinical trials and development for market of the
pharmaceutical agents) are followed to date, the distribution of funding at each
stage have changed due to the changing global market and healthcare policies [25].
Even though pharmaceutical companies relatively survived the recession phase
of the early twenty-first century, a significant amount of budget cuts in R&D and
new drug development pipeline was evident [26]. Post-recession, in the course
of recovery, the collaborative efforts of the pharmaceutical and IT industry have
brought state-of-the-art analytical tools that can pull multifaceted data in large
quantities and predict the patients’ needs and market trends [27]. This has enabled

Drug Discovery and Development - New Advances

the pharmaceutical companies to reorganize the drug discovery and development
programs in a more efficient and cost-effective way. Furthermore, market research
has contributed to global pharmaceutical growth that is projected to reach 1.18
trillion US dollars by 2024 [28].
The main reason of this success is the data-driven integration of every major
component of pharmaceutical industries with the healthcare industries that
includes hospitals, doctors, patients, and insurance companies along with the
regular drug discovery units. This has transformed the classical linear drug discov-
ery road (Figure ) into a complex multidimensional map (Figure ), where the
whole industry is revolving around the power of the market analysis in a symbiotic
fashion. Though the specific needs of different companies are different depending
on their size, resources, and target market, the cumulative fabric of symbiosis is
common [29]. The existing market data has accelerated the process of “new target
identification.” It has also helped in repurposing existing and abandoned therapeu-
tics from different phases of drug development in an unprecedented way [30, 31].
Proper analysis of healthcare data and labor market research have shown positive
impact on government policies in allotting and redistributing funds for healthcare
industries and basic academic research that are closely associated to drug discovery
research, which consequently helps the pharmaceutical market to grow [32, 33].
The huge success in genomics research, high-throughput screening (HTS) robotics,
and gene sequencing technologies resulted a pull of publication that have reported
synthesis or extraction of a cumulative of over 90 million drug-like compounds
[34]. Moreover, advances in large-scale cell and tissue imaging have enabled precise
location determination of the drugs and measured variety of phenotypes in cells
and whole organism [35]. These advances in hardware instruments, research
methodologies, and data processing synergistically contribute at various stages
of drug development. The application of deep learning in leveraging these large-
scale heterogeneous database is now an integral part of industrial pharmaceutical
research [36]. Although machine learning (ML) is at its infant stage, it has already
Figure 1.
Classical components of drug discovery.


Introductory Chapter: e Modern-Day Drug Discovery
DOI: hp://dx.doi.org/10.5772/intechopen.90922
reduced the library sizes for HTS and helped to understand complex multiomic data
[37, 38]. The rapid progress of different ML methods will have considerable impact
on future therapies [39].
. Importance of PK/PD in modern-day drug discovery
The historical prototype for clinical drug development was to conduct a few
Phase 1 studies followed by a couple of Phase 2 studies consequently leading to
multiple expensive Phase 3 trials to demonstrate the efficacy of the drug candidate.
With the changing landscape and regulatory requirements, the number of clinical
studies to elucidate multiple questions related to drug properties such as the
mechanism of actions, pharmacokinetics (PK), pharmacodynamics (PD), and drug
metabolism increased overwhelmingly prior to Phase 3 studies. The increase in the
number of clinical trials has made drug development more lengthy and exorbitant.
To overcome this limitation and reach patients promptly, it is imperative to utilize
advanced technologies and approaches. One such approach is the PK/PD guided
drug development. PK/PD modeling has been extensively employed to generate
first-in-human dose predictions and selecting optimal doses for Phase 2 and Phase
3 trials. PK/PD modeling also plays an instrumental role in identifying if any dose
adjustments are needed in special populations such as pediatrics and geriatrics and
patients with hepatic or renal impairments [40, 41]. Additionally, PK/PD model-
informed drug development (MIDD) has gained increasing momentum in recent
years and is extensively used across pharmaceutical industries globally.
MIDD has become a crucial tool after receiving formal recognition in
Prescription Drug User Fee Act (PDUFA) VI, thus paving a path forward to opti-
mize drug dosing prior to approval and post-marketing and in special populations
Figure 2.
Modern-day symbiotic fabric of drug discovery.

Drug Discovery and Development - New Advances

Author details
ParthaKarmakar1, AshitTrivedi2 and VishwanathGaitonde3*
1 Department of Radiology, Washington University School of Medicine, St. Louis,
MO, USA
2 Clinical Pharmacology Modeling and Simulations, Amgen, Thousand Oaks, CA,
USA
3 Chemical Research and Development, Cambrex High Point, Inc., High Point, NC,
USA
*Address all correspondence to: vishwanath.gaitonde@cambrex.com
in the absence of dedicated clinical trials. Dose optimization and clinical trial design
have been most established domains of MIDD; new technologies such as artificial
intelligence, ML, and real-world data (RWD), wearables along data science, have
the potential to transform MIDD.
ML approaches provide a set of tools that can improve decision-making for
well-specified questions with abundant, high-quality data. While using ML in the
early stages of drug design, target selection, and high-throughput screening is
almost standard today, the potential of ML during drug development has not been
recognized. The observed data/evidence obtained during the developmental phase
does not necessarily answer all the questions; thus the scope of MIDD is largely
expanded with analysis of RWD to generate real-world evidence (RWE) to resolve
these unanswered questions. Although RWD is obtained under less-controlled
settings requiring proper interpretation of the findings, it should be considered as
an attractive tool appealing for MIDD [42].
The emerging new techniques, such as portable devices, wearables, and applica-
tions (apps), may improve the dosing accuracy for patients and the quality of the
collected medical information in real-world medical practice. These tools may
improve the quality of electronic health records, making real-world data a reliable
source for drug development and dose optimization or individualization. All these
tools will make real-world data/real-world evidence a more appealing source for
MIDD [43].
Along with the power of data analytics, advances in computational chemis-
try, and new diagnostic techniques, PK/PD modeling tools have also influenced
the drug discovery research and development. These advances assist to build a
comprehensive protein-receptor database, thereby enabling a defined library size
for designing and optimization of a lead molecule. Along with the classical small
molecule drug discovery and development, many protein and antibody-based
pharmaceuticals have appeared as blockbuster drugs.
© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms
of the Creative Commons Attribution License (http://creativecommons.org/licenses/
by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,
provided the original work is properly cited.


Introductory Chapter: e Modern-Day Drug Discovery
DOI: hp://dx.doi.org/10.5772/intechopen.90922
[1] Sharma T, Rawal G.Role of
ayurveda in tumorigenesis: A brief
review. International Journal of Green
Pharmacy (IJGP). 2012;(2):93-101
[2] Mamtani R, Mamtani R.
Ayurveda and yoga in cardiovascular
diseases. Cardiology in Review.
2005;(3):155-162
[3] Khan S, Balick MJ. Therapeutic plants
of Ayurveda: A review of selected clinical
and other studies for 166 species. The
Journal of Alternative & Complementary
Medicine. 2001;(5):405-515
[4] Glazier A.A landmark in the
history of Ayurveda. The Lancet.
2000;(9235):1119
[5] Farquhar J.Knowing Practice: The
Clinical Encounter of Chinese Medicine.
London: Routledge; 2018
[6] Law BYK, Wu AG, Wang MJ,
Zhu YZ. Chinese medicine: A hope
for neurodegenerative diseases?
Journal of Alzheimer’s Disease.
2017;(s1):S151-S160
[7] Ackerknecht EH, Haushofer L.A
Short History of Medicine. Baltimore:
Johns Hopkins University Press; 2016
[8] Saad B, Azaizeh H, Said O.Tradition
and perspectives of Arab herbal
medicine: A review. Evidence-based
Complementary and Alternative
Medicine. 2005;(4):475-479
[9] Bynum W, Porter R.Arab-Islamic
medicine. In: Companion Encyclopedia
of the History of Medicine. London:
Routledge; 2013. pp.702-753
[10] Cates J, Christie R.Subacute
bacterial endocarditis. A review of 442
patients treated in 14 Centres appointed
by the penicillin trials Committee of the
Medical Research Council. Quarterly
Journal of Medicine. 1951;(78):93-130
[11] Duggleby HJ, Tolley SP, Hill CP,
Dodson EJ, Dodson G, Moody PC.
Penicillin acylase has a single-amino-
acid catalytic centre. Nature.
1995;(6511):264
[12] Heath JR, Ribas A, Mischel PS.
Single-cell analysis tools for drug
discovery and development. Nature
Reviews Drug Discovery. 2016;(3):204
[13] Kelm JM, Lal-Nag M, Sittampalam
GS, Ferrer M. Translational invitro
research: Integrating 3D drug discovery
and development processes into the
drug development pipeline. Drug
Discovery Today. 2019;(1):26-30
[14] Walker N.Impacts of Glocalization
on the Pharmaceutical Industry.
2019. Available from: https://www.
pharmasalmanac.com/articles/
impacts-of-glocalization-on-the-
pharmaceutical-industry
[15] Pharmaceutical market: Worldwide
revenue 2001-2018. 2019. Available
from: https://www.statista.com/
statistics/263102/pharmaceutical-
market-worldwide-revenue-since-2001
[16] Yaari Z, Da Silva D, Zinger A,
Goldman E, Kajal A, Tshuva R, etal.
Theranostic barcoded nanoparticles for
personalized cancer medicine. Nature
Communications. 2016;:13325
[17] Lee H, Lee Y, Song C, Cho HR,
Ghaffari R, Choi TK, etal. An endoscope
with integrated transparent
bioelectronics and theranostic
nanoparticles for colon cancer treatment.
Nature Communications. 2015;:10059
[18] Karmakar P, Lee K, Sarkar S,
Wall KA, Sucheck SJ.Synthesis of a
liposomal MUC1 glycopeptide-based
immunotherapeutic and evaluation of
the effect of L-rhamnose targeting on
cellular immune responses. Bioconjugate
Chemistry. 2015;(1):110-120
References

Drug Discovery and Development - New Advances

[19] Karmakar P, Gaitonde V.Promising
recent strategies with potential
clinical translational value to combat
antibacterial resistant surge. Medicine.
2019;(1):21
[20] Catalano JG, Gaitonde V, Beesu M,
Leivers AL, Shotwell JB.Phenoxide
leaving group SNAr strategy for the
facile preparation of 7-amino-3-aryl
pyrazolo [1, 5-a] pyrimidines from a
3-bromo-7-phenoxypyrazolo [1, 5-a]
pyrimidine intermediate. Tetrahedron
Letters. 2015;(44):6077-6079
[21] Nishimura Y, Gautam R, Chun T-W,
Sadjadpour R, Foulds KE, Shingai M,
etal. Early antibody therapy can induce
long-lasting immunity to SHIV.Nature.
2017;(7646):559
[22] Gaitonde V,Sucheck SJ.
Antitubercular drugs based on
carbohydrate derivatives. Carbohydrate
Chemistry: State of the Art and
Challenges for Drug Development: An
Overview on Structure, Biological Roles,
Synthetic Methods and Application as
Therapeutics. 2015:441-478
[23] Thanna S, Lindenberger JJ,
Gaitonde VV, Ronning DR, Sucheck SJ.
Synthesis of 2-deoxy-2, 2-difluoro-α-
maltosyl fluoride and its X-ray structure
in complex with Streptomyces coelicolor
GlgEI-V279S.Organic & Biomolecular
Chemistry. 2015;(27):7542-7550
[24] Hossain MK, Vartak A, Karmakar P,
Sucheck SJ, Wall KA .Augmenting vaccine
immunogenicity through the use
of natural human anti-rhamnose
antibodies. ACS Chemical Biology.
2018;(8):2130-2142
[25] Gilliland CT, Zuk D, Kocis P,
Johnson M, Hay S, Hajduch M, etal.
Putting translational science on to a
global stage. Nature Reviews Drug
Discovery. 2016;(4):217
[26] Clark JW.Financial Narratives of
US Biotechnology Companies Before,
During, and After the Great Recession.
2015. Available from: https://fisherpub.
sjfc.edu/cgi/viewcontent.cgi?article=1
218&context=education_etd; https://
fisherpub.sjfc.edu/education_etd/217
[27] Joshi RR, Sonavane U, Jani V,
Saxena A, Koulgi S, Uppuladinne M,
etal. Turbo Analytics: Applications of
Big Data and HPC in Drug Discovery. In
Structural Bioinformatics: Applications
in Preclinical Drug Discovery
Process. Switzerland: Springer; 2019.
pp.347-374
[28] EvaluatePharma World Preview
2019, Outlook to 2024. 2019. Available
from: https://www.evaluate.com/
thought-leadership/pharma/
evaluatepharma-world-preview-2019-
outlook-2024
[29] Wagner JA, Dahlem AM,
Hudson LD, Terry SF, Altman RB,
Gilliland CT, etal. Application of
a dynamic map for learning,
communicating, navigating, and
improving therapeutic development.
Clinical and Translational Science.
2018;(2):166-174
[30] Karaman B, Sippl W.Computational
drug repurposing: Current trends.
Current Medicinal Chemistry.
2019;(28):5389-5409
[31] Nordon G, Koren G, Shalev V,
Horvitz E, Radinsky K.Proceedings
of the AAAI Conference on Artificial
Intelligence. Separating Wheat from
Chaff: Joining Biomedical Knowledge
and Patient Data for Repurposing
Medications. Vol. 33, No 01: AAAI-19,
IAAI-19, EAAI-20; 2019. IAAI Technical
Track: Emerging Papers. Available from:
https://www.aaai.org/ojs/index.php/
AAAI/article/view/5017
[32] Cinaroglu S.Politics and health
outcomes: A path analytic approach.
The International Journal of
Health Planning and Management.
2019;(1):e824-e843


Introductory Chapter: e Modern-Day Drug Discovery
DOI: hp://dx.doi.org/10.5772/intechopen.90922
[33] Zolbanin HM, Delen D, Sharma SK.
The strategic value of big data analytics
in health care policy-making.
International Journal of E-Business
Research (IJEBR). 2018;(3):20-33
[34] Kim S.Getting the most out
of PubChem for virtual screening.
Expert Opinion on Drug Discovery.
2016;(9):843-855
[35] Boutros M,Heigwer F,
Laufer C. Microscopy-based
high-content screening. Cell.
2015;(6):1314-1325
[36] David L, Arús-Pous J, Karlsson J,
Engkvist O, Bjerrum EJ, Kogej T, etal.
Applications of deep-learning
in exploiting large-scale and
heterogeneous compound data in
industrial pharmaceutical research.
Frontiers in Pharmacology. 2019;:1-16
[37] McGaughey GB, Sheridan RP, Bayly CI,
Culberson JC, Kreatsoulas C, Lindsley S,
etal. Comparison of topological, shape,
and docking methods in virtual
screening. Journal of Chemical
Information and Modeling.
2007;(4):1504-1519
[38] Johnson KW, Shameer K, Glicksberg
BS, Readhead B, Sengupta PP,
Björkegren JL, etal. Enabling precision
cardiology through multiscale biology
and systems medicine. JACC: Basic to
Translational Science. 2017;(3):311-327
[39] Ekins S, Puhl AC, Zorn KM,
Lane TR, Russo DP, Klein JJ, etal.
Exploiting machine learning for end-to-
end drug discovery and development.
Nature Materials. 2019;(5):435
[40] Bonate PL,Howard DR.
Pharmacokinetics in Drug
Development: Problems and Challenges
in Oncology. Vol. 4. Switzerland:
Springer; 2016
[41] Katherine Dunnington NB,
Brandquist C, Cardillo-Marricco N,
Di Spirito M, Grenier J.Application
of Pharmacokinetics in Early Drug
Development. Rijeka: IntechOpen; 2018
[42] Marshall S, Madabushi R, Manolis E,
Krudys K, Staab A, Dykstra K, etal.
Model-informed drug discovery
and development: Current industry
good practice and regulatory
expectations and future perspectives.
CPT: Pharmacometrics & Systems
Pharmacology. 2019;(2):87-96
[43] Wang Y, Zhu H, Madabushi R,
Liu Q , Huang SM, Zineh I.Model-
informed drug development: Current
US regulatory practice and future
considerations. Clinical Pharmacology
& Therapeutics. 2019;(4):899-911