Chemical Sciences in Early Drug Discovery

Medicinal Chemistry 2.0

Pierfausto Seneci

• Publisher: Elsevier
• Year: 2018
• Print ISBN: 9780080994208
• eBook ISBN: 9780080999289
• Topic: Physical Sciences
• Subtopic: Chemistry
• Index: Chemistry

Chemical Sciences in Early Drug Discovery: Medicinal Chemistry 2.0 describes how new technologies and approaches can be used to improve the probability of success in fulfilling the perennial goal of finding and developing new drugs. Drawing on the author's extensive experience consulting and teaching in medicinal chemistry, the book outlines ways in which medicinal chemistry is widening its reach to meet modern demands, and how modern technologies and approaches are facilitating this growth into new fields. Supported by examples throughout, the book is a practical resource for organic-medicinal chemists, biological chemists and pharmacologists involved in drug discovery.- Reviews the key application of chemistry in drug discovery for both medicinal and non-medicinal chemists, clarifying and explaining the role of medicinal chemistry in supporting the modern drug discovery pipeline- Shows how a wider medicinal chemistry view is essential for anyone in an integrated drug discovery project looking to reduce costs and save time- Provides the critical success factors needed to successfully identify hits from both biological and chemical perspectives

Chapter 1

Step I: Target Identification

Abstract

The craving need for novel targets in drug discovery, as gateways toward the cure of poorly tractable and intractable diseases, is illustrated here. The first section covers biology-based target identification methods in drug discovery through the last few decades. It presents functional and positional cloning, massive DNA sequencing, systems biology, RNA interference, and CRISPR-Cas9 genome editing. The following section deals with chemistry in target identification. It underlines the role of chemical diversity in supporting the identification of therapeutic targets. It covers natural products as high content probes, target-based and phenotype-based screening, forward and reverse chemical genetics approaches using drug-like collections in target identification. The last section describes the discovery of monastrol and the identification of kinesin Eg5 as an example of successful chemistry-supported target identification.

Keywords

Cloning; Sequencing; Systems biology; RNA interference; CRISPR Cas9; Natural products; Compound collections; Chemical diversity; Chemical genetics; Monastrol; Kinesins

Molecular targets and their identification are the foundation of R&D pharmaceutical projects (step 1, Fig. 1.1). No matter what is done later, choosing a wrong target dooms any R&D project. Conversely, a disease-related target leads—if properly characterized for its in vitro and in vivo physiological and pathological role, and modulated with drug-like biological or chemical entities—to therapeutically relevant outcomes.

Fig. 1.1 The R&D pharmaceutical process.

The definition of “druggable genome” dates back to 2002.¹ If at least one gene family member interacts with one or more biological or chemical compounds, the whole gene family is druggable. A ≈ 3000 druggable gene number was predicted.¹ A similar size was estimated by large-scale gene knockout studies for disease-related, putative drug target genes,² i.e., ≈ 10% of the estimated 30K gene-sized human genome possessing putative disease-modifying features.³ The overlapping population of druggable, disease-related genes was then set between 600 and 1500 (Fig. 1.2, top).

Fig. 1.2 The druggable genome: size estimation, early 2000 and today.

Today’s view on druggable genome and drug targets has changed. The size of the human genome is smaller (≈ 20–25K genes),⁴ but the recent modulation/“druggability” of therapeutically relevant protein-protein interactions⁵ and the success of biological drugs⁶ has increased the druggable genome size to ≈ 5K genes. Reliable and efficient validation tools (see next chapter) have increased the number of disease-related molecular targets—once more, to ≈ 5K targets. Thus, a larger set of overlapping druggable, disease-related targets exists—let’s say ≥ 2K targets (Fig. 1.2, bottom). This size should further increase in future due to druggability- and target validation-related innovations.

An approved drug gives full confidence in its molecular target. A rigorous analysis⁷ identified 555 gene targets of past and present drugs. Drug candidates undergoing clinical trials inspire similar confidence for their targets. The same report⁷ listed 475 gene targets of clinically tested compounds (small molecules or biologicals).

Large pharmaceutical companies (“big pharmas”) usually work on “me too” candidates⁸ acting on market- or clinically validated targets. Their risk of failure is lower, while their market potential—powered by the marketing capacity of big pharmas—remains significant.

Even if each of the 475 targets in clinical validation would see the approval of one of its modulators as drugs, the 1030 validated targets would represent ≈ 50% of druggable, disease-related genes. The identification and validation of the remaining targets will benefit from chemistry-based approaches and tools.

1.1 The Foundation: Molecular Biology, No Chemistry

Gene-encoded protein targets stem from the unraveling of DNA structure in 1953,⁹ of three RNA codons in 1962,¹⁰ and from DNA sequencing in 1977.¹¹ Traditional medicine remedies,¹² such as aspirin,¹³ were made available to patients much earlier. The molecular targets of most pre-Watson and Crick drugs were identified after their therapeutic application. Even today the lack of a mechanism of action (MoA) does not prevent the development of a drug, if potent and safe in humans.¹⁴ A known MoA, though, explains preclinical and clinical effects of drug candidates, and supports their postmarketing surveillance among patients.

A molecular target-driven working hypothesis is important to any pharma R&D project, even at the earliest stage. Sound assumptions in terms of target-related efficacy and safety increase the confidence (and possibly reduce the risk of failure) for their modulators.

Recombinant DNA¹⁵ and polymerase chain reaction (PCR)¹⁶ technologies allowed gene-driven target identification (TI). Initially, targets were identified through functional cloning¹⁷ (Fig. 1.3, left). Disease-related biochemical abnormalities of proteins prompted researchers to isolate and characterize the altered proteins. The structure of their encoding genes and their chromosomal location were determined. Sickle cell anemia and beta hemoglobin/HBB¹⁸ are examples of TI via functional cloning.

Fig. 1.3 Gene-driven TI: functional cloning, positional cloning, and massive sequencing.

Later, positional cloning¹⁹ required the approximate chromosomal position of putative targets, identified by scanning up to 5 millions base pairs for disease-related mutations. Positional cloning-based TI relied upon chromosomal rearrangements (e.g., chronic myelogenous leukemia/CML-breakpoint cluster region-Abelson/BCR-ABL²⁰), including extended trinucleotide repeats (e.g., Machado-Joseph disease/MJD-ataxin3/ATXN3²¹) (Fig. 1.3, middle). Even without major DNA rearrangements, positional cloning (e.g., spinal muscular atrophy/SMA-survival motor neuron/SMN gene²²), or a hybrid positional/functional approach¹⁸ (e.g., Marfan syndrome-fibrillin/FBN²³) were successful in TI by mid-late '90s.

Technology improvements led to massive parallel DNA sequencing.²⁴ Times-to-genomes were red...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Introduction
• Chapter 1: Step I: Target Identification
• Chapter 2: Step II: Target Validation
• Chapter 3: Step IIIa: Biological Hit Discovery Through High-Throughput Screening (HTS): Random Approaches and Rational Design
• Chapter 4: Step IIIb: The Drug-Like Chemical Diversity Pool: Diverse and Targeted Compound Collections
• Index