Drugs

With antimicrobial resistance to current drugs steadily rising, the development of new antibiotics with novel mechanisms of action has become an imperative. The majority of life-threatening infections worldwide are caused by "ESKAPE" pathogens which are encountered in more than 40% of hospital-acquired infections, and are resistant to the majority of commonly used antibiotics. Naturally occurring cyclic depsipeptides, microbial secondary metabolites that contain one or more ester bonds in addition to amide bonds, have emerged as an important source of pharmacologically active compounds or lead structures for the development of novel antibiotics. Some of those peptides are either already marketed (daptomycin) or in advanced stages of clinical development (ramoplanin). Structurally simple, yet potent, fusaricidin/LI-F and lysobactin families of naturally occurring antibiotics represent particularly attractive candidates for the development of new antibacterial agents capable of overco ming infections caused by multidrug-resistant bacteria. These natural products exhibit potent antimicrobial activity against a variety of clinically relevant fungi and Gram-positive bacteria. Therefore, access to these classes of natural products and their synthetic analogs, combined with elucidation of their mode of action represent important initial steps toward full exploitation of their antmicrobial potential. This dissertation describes a general approach toward the solid-phase synthesis of fusaricidin/LI-F and lysobactin analogs and an extensive structure-activity relationship (SAR) study. We have devised a simple and robust preparation strategy based on standard Fmoc solid-phase peptide synthesis protocols.

In this dissertation we will present a stochastic optimization algorithm and use it and other mathematical techniques to tackle problems arising in medicinal chemistry. In Chapter 1, we present some background about stochastic optimization and the Accelerated Random Search (ARS) algorithm. We then present a novel improvement of the ARS algorithm, DIrected Accelerated Random Search (DARS), motivated by some theoretical results, and demonstrate through numerical results that it improves upon ARS. In Chapter 2, we use DARS and other methods to address issues arising from the use of mixture-based combinatorial libraries in drug discovery. In particular, we look at models associated with the biological activity of these mixtures and use them to answer questions about sensitivity and robustness, and also present a novel method for determining the integrity of the synthesis. Finally, in Chapter 3 we present an in-depth analysis of some statistical and mathematical techniques in combinatorial chemistry, including a novel probabilistic approach to using structural similarity to predict the activity landscape.

In 2006, there were over 39 million people with Human Immunodeficiency Virus (HIV) worldwide, and 2.9 million HIV-related deaths. Currently, a cocktail of drugs administered via injection (HAART) has some efficacy in treating HIV, but does not eradicate HIV from infected individuals and has long-term side effects. In addition, drug-resistant variants of HIV are emerging. In an effort to help develop orally administered anti-HIV drugs, we examined membrane permeability of four scaffold peptides (synthesized by a researcher at Scripps Florida) into T-cells. One peptide (KE1-72A) entered cells with 100% efficiency; a second (KE1-72B) showed minimal cell penetration. Two other peptides (KE1-72C and KE1-72D), when chemically conjugated to an HIV fusion inhibitor, also showed minimal cell penetration. Further research is needed to determine whether the peptide KE1-72A may potentially be useful in orally delivered anti-HIV drugs.