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Chapter 2 Methods of obtaining drugs from medicinal and aromatic plants 103
tween genotype and phenotype. While traditional approaches often focused on a limited number of proteins, innovations in mass spectrometry and related technologies
now allow for the holistic examination of biological systems as unified entities. These
technological advancements have also driven the development of new bioinformatics
analysis methods, enabling deeper molecular-level insights in proteomics. Although
many bioinformatics techniques commonly used in proteomics are adapted from
other omics fields, the unique nature of proteomic data necessitates the development
of specialized analytical strategies [202].
2.5 Future scope
The drug discovery process has historically been long and labor-intensive, requiring
significant effort in sourcing. This process begins with identifying a lead compound
for a specific drug target, followed by rigorous optimization and preclinical studies in
animal models to assess efficacy and toxicity. These initial steps typically take 5–6
years, and only 1 out of every 5,000 lead compounds advances to human trials. Moreover, obtaining final approval from regulatory bodies, such as the U.S. Food and Drug
Administration (FDA), adds to the timeline, resulting in an average time of 12 years
and high costs for a drug to reach the market [203].
AI has emerged as a transformative force in the pharmaceutical industry, bringing the ability to simplify drug development and lower related costs. Experts in the
field predict that AI-powered drug discovery methods can shorten the process, and
drugs developed by AI could reach the market in a relatively short time [204].
If AI advancements are applied to the plant-based drug discovery process, they
could enable the rapid identification of drug candidates, offering time savings compared to traditional methods. Using an AI-supported platform, a molecule for obsessive-compulsive disorder was developed in just one year, marking it as the first AIinvented molecule to enter human trials. Integrating AI with databases containing
plant-derived chemical compounds offers a promising path for innovation in drug discovery. Rich in bioactive compounds, traditional medicine can benefit from AI’s analytical capabilities to identify and optimize potential therapeutic agents. By leveraging
machine learning algorithms, researchers can predict compound efficacy and toxicity,
thus accelerating the discovery of new treatments. Successfully combining AI with traditional medicine could bridge gaps in current drug development practices. AIsupported platforms can analyze large datasets to reveal relationships between compounds and therapeutic effects, offering a more efficient alternative to traditional
trial-and-error methods. This approach not only accelerates drug development but
also has the ability to enhance the treatment of rare and neglected illnesses [205].
104 İrem Yıldız Özbaş, Severina Pacifico, and Emre Özbaş
2.6 Conclusion
Medicinal and aromatic plants have been used for therapeutic purposes for centuries.
Today, many approved drugs are derived from plants. Since plants host a vast phytochemical diversity compared to synthetic molecules, they are an immense source for
new drug development. Over the years, research on plants has accumulated enough
knowledge to form natural product libraries. However, the existence of plants worldwide whose effects and contents remain unexplored makes them an almost unlimited
resource.
New drug discovery and development, whether based on synthetic or natural
compounds, is a complex, difficult, and lengthy process. Additionally, the complex nature of plants and the intricate chemical structure of their components make the drug
development process from plants even more challenging. The process of drug discovery from plants generally consists of identifying the source plant, collecting plant material, performing preliminary treatments, extraction, bioactivity studies, isolation
and purification, biological experiments, clinical trials, and optimization. At each
stage, various obstacles are encountered, depending on the plant’s characteristics and
the methods used. If these obstacles are not overcome, even if the plant’s therapeutic
properties are discovered, they cannot be translated into clinical applications. Despite
the development of various innovative methods and technologies to overcome these
challenges, some issues still persist. Among these issues, the most critical are the lack
of standardization, the inability to isolate and purify pure chemical compounds, insufficient elucidation of biological mechanisms, and the limited success of transitioning
to controlled clinical trials with very few molecules. To facilitate, accelerate, and ultimately succeed in drug discovery and development from plants, innovative drug design methods are essential.
Identifying the source plant is one of the most crucial steps in the drug discovery
procedure from medicinal and aromatic plants, as it will influence the success of subsequent stages. When identifying candidate plants or plant compounds, the utilization
of high-throughput screening (HTS) technologies and virtual screening methods not
only increases success but can also significantly speed up the process. Additionally,
systematic ethnopharmacological studies will contribute to the plant identification
process.
In some diseases and conditions, isolated single compounds from plants do not
reflect the therapeutic effect exhibited by the plant. This is due to the synergistic effects exhibited by the components in the plant’s complex structure. To overcome this
issue, combinatorial chemical approaches should be used when evaluating drug candidate compounds obtained from plants. The innovative technologies of the computational molecular design era, along with new analytical and computational techniques,
have opened new horizons in the process from plants to drugs.
New analytical technologies and combinatorial chemistry, including artificial intelligence (AI) and new computational and screening methods, omics technologies for
Chapter 2 Methods of obtaining drugs from medicinal and aromatic plants 105
studying interactions between bioactive molecules and targets, microfluidics for designing biological models, and innovative drug design, are methods that have the potential for great success in drug discovery from medicinal and aromatic plants and
possess unique potential in future studies. Considering that natural products are
unique and rich sources for drug discovery, the integration of advanced methods will
inevitably lead to an increase in plant-based drugs in the future.
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