Drug discovery and development is a time-consuming, complex, and
expensive process. Usually, it takes about 15 years in the best scenario since drug
candidates have a high attrition rate. Therefore, drug development projects rarely take
place in low and middle-income countries (LMICs). Traditionally, this process consists
of four sequential stages: (1) target identification and early drug discovery, (2)
preclinical studies, (3) clinical development, and (4) review, approval and monitoring
by regulatory agencies. During the last decades, computational tools have offered interesting opportunities for
Research and Development (R & D) in LMICs, since these techniques are affordable,
reduce wet lab experiments in the first steps of the drug discovery process, reduce
animal testing by aiding experiment design, and also provide key knowledge involving
clinical data management as well as statistical analysis.
This book chapter aims to highlight different computational tools to enable early drug
discovery and preclinical studies in LMICs for different pathologies, including cancer.
Several strategies for drug target selection are discussed: identification, prioritization
and validation of therapeutic targets; particularly focusing on high-throughput analysis
of different “omics” approaches using publicly available data sets. Next, strategies to
identify and optimize novel drug candidates as well as computational tools for costeffective drug repurposing are presented. In this stage, chemoinformatics is a key
emerging technology. It is important to note that additional computational methods can
be used to predict possible uses of identified human-aimed drugs for veterinary
purposes.
Application of computational tools is also possible for predicting pharmacokinetics and
pharmacodynamics as well as drug-drug interactions. Drug safety is a key issue and it
has a profound impact on drug discovery success.
Finally, artificial intelligence (AI) has also served as a potential tool for drug design
and discovery, expected to be a revolution for drug development in several diseases. It is important to note that the development of drug discovery projects is feasible in
LMICs and in silico tools are expected to potentiate novel therapeutic strategies in
different diseases. This book chapter aims to highlight different computational tools to enable early drug
discovery and preclinical studies in LMICs for different pathologies, including cancer.
Several strategies for drug target selection are discussed: identification, prioritization
and validation of therapeutic targets; particularly focusing on high-throughput analysis
of different “omics” approaches using publicly available data sets. Next, strategies to
identify and optimize novel drug candidates as well as computational tools for costeffective drug repurposing are presented. In this stage, chemoinformatics is a key
emerging technology. It is important to note that additional computational methods can
be used to predict possible uses of identified human-aimed drugs for veterinary
purposes. Application of computational tools is also possible for predicting pharmacokinetics and
pharmacodynamics as well as drug-drug interactions. Drug safety is a key issue and it
has a profound impact on drug discovery success.
Finally, artificial intelligence (AI) has also served as a potential tool for drug design
and discovery, expected to be a revolution for drug development in several diseases.Application of computational tools is also possible for predicting pharmacokinetics and
pharmacodynamics as well as drug-drug interactions. Drug safety is a key issue and it
has a profound impact on drug discovery success.
Finally, artificial intelligence (AI) has also served as a potential tool for drug design
and discovery, expected to be a revolution for drug development in several diseases.
