Peptide-Drug Conjugates: A New Hope for Cancer Management

Cancer remains the leading cause of death worldwide despite advances in treatment options for patients. As such, safe and effective therapeutics are required. Short peptides provide advantages to be used in cancer management due to their unique properties, amazing versatility, and progress in biotechnology to overcome peptide limitations. Several appealing peptide-based therapeutic strategies have been developed. Here, we provide an overview of peptide conjugates, the better equivalents of antibody-drug conjugates, as the next generation of drugs for required precise targeting, enhanced cellular permeability, improved drug selectivity, and reduced toxicity for the efficient treatment of cancers. We discuss the basic components of drug conjugates and their release action, including the release of cytotoxins from the linker. We also present peptide-drug conjugates under different stages of clinical development as well as regulatory and other challenges.     

Flexizyme‐Mediated Genetic Reprogramming As a Tool for Noncanonical Peptide Synthesis and Drug Discovery

Noncanonical peptides occur frequently in Nature, and often display high bioactivity. However, the lack of tractable systems for the synthesis of diverse libraries of such peptides has thus far hampered their development as drugs. Genetic reprogramming techniques, in which noncanonical amino acids may be incorporated into peptides, have largely removed this limitation. This Concept article outlines the development of these techniques with an emphasis on drug discovery.     

Drug Repurposing for COVID-19: A Review and a Novel Strategy to Identify New Targets and Potential Drug Candidates

In December 2019, the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19) was first identified in the province of Wuhan, China. Since then, there have been over 400 million confirmed cases and 5.8 million deaths by COVID-19 reported worldwide. The urgent need for therapies against SARS-CoV-2 led researchers to use drug repurposing approaches. This strategy allows the reduction in risks, time, and costs associated with drug development. In many cases, a repurposed drug can enter directly to preclinical testing and clinical trials, thus accelerating the whole drug discovery process. In this work, we will give a general overview of the main developments in COVID-19 treatment, focusing on the contribution of the drug repurposing paradigm to find effective drugs against this disease. Finally, we will present our findings using a new drug repurposing strategy that identified 11 compounds that may be potentially effective against COVID-19. To our knowledge, seven of these drugs have never been tested against SARS-CoV-2 and are potential candidates for in vitro and in vivo studies to evaluate their effectiveness in COVID-19 treatment.     

Recent Advances and Trends in Chemical CPP–Drug Conjugation Techniques

This review summarizes recent developments in conjugation techniques for the synthesis of cell-penetrating peptide (CPP)–drug conjugates targeting cancer cells. We will focus on small organic molecules as well as metal complexes that were used as cytostatic payloads. Moreover, two principle ways of coupling chemistry will be discussed direct conjugation as well as the use of bifunctional linkers. While direct conjugation of the drug to the CPP is still popular, the use of bifunctional linkers seems to gain increasing attention as it offers more advantages related to the linker chemistry. Thus, three main categories of linkers will be highlighted, forming either disulfide acid-sensitive or stimuli-sensitive bonds. All techniques will be thoroughly discussed by their pros and cons with the aim to help the reader in the choice of the optimal conjugation technique that might be used for the synthesis of a given CPP–drug conjugate     

Multinuclear MRI in Drug Discovery

The continuous development of magnetic resonance imaging broadens the range of applications to newer areas. Using MRI, we can not only visualize, but also track pharmaceutical substances and labeled cells in both in vivo and in vitro tests. ¹H is widely used in the MRI method, which is determined by its high content in the human body. The potential of the MRI method makes it an excellent tool for imaging the morphology of the examined objects, and also enables registration of changes at the level of metabolism. There are several reports in the scientific publications on the use of clinical MRI for in vitro tracking. The use of multinuclear MRI has great potential for scientific research and clinical studies. Tuning MRI scanners to the Larmor frequency of a given nucleus, allows imaging without tissue background. Heavy nuclei are components of both drugs and contrast agents and molecular complexes. The implementation of hyperpolarization techniques allows for better MRI sensitivity. The aim of this review is to present the use of multinuclear MRI for investigations in drug delivery.     

Drug Repurposing and De Novo Drug Discovery of Protein Kinase Inhibitors as New Drugs against Schistosomiasis

Schistosomiasis is a neglected tropical disease affecting more than 200 million people worldwide. Chemotherapy relies on one single drug, praziquantel, which is safe but ineffective at killing larval stages of this parasite. Furthermore, concerns have been expressed about the rise in resistance against this drug. In the absence of an antischistosomal vaccine, it is, therefore, necessary to develop new drugs against the different species of schistosomes. Protein kinases are important molecules involved in key cellular processes such as signaling, growth, and differentiation. The kinome of schistosomes has been studied and the suitability of schistosomal protein kinases as targets demonstrated by RNA interference studies. Although protein kinase inhibitors are mostly used in cancer therapy, e.g., for the treatment of chronic myeloid leukemia or melanoma, they are now being increasingly explored for the treatment of non-oncological conditions, including schistosomiasis. Here, we discuss the various approaches including screening of natural and synthetic compounds, de novo drug development, and drug repurposing in the context of the search for protein kinase inhibitors against schistosomiasis. We discuss the status quo of the development of kinase inhibitors against schistosomal serine/threonine kinases such as polo-like kinases (PLKs) and mitogen-activated protein kinases (MAP kinases), as well as protein tyrosine kinases (PTKs).     

Polymer Carriers for Anticancer Drugs Targeted to EGF Receptor

A novel actively targeted polymer carrier for anticancer drugs based on an N‐(2‐hydroxypropyl)methacrylamide copolymer (PHPMA) is proposed. An oligopeptide sequence GE7, attached to the polymer, is a specific ligand for the EGF receptor overexpressed on most tumor cells. Co‐attachment of selected chemotherapeutics will therefore lead to formation of tumor‐specific polymer therapeutics, further enhanced by the EPR effect. FACS measurements prove elevated binding activity of the fluorescently labeled PHPMA/GE7 conjugate in EGFR‐rich cells (FaDu, MCF‐7), compared to conjugates of scrambled peptides. Cell lines with low EGFR level (SW620, B16F10) bind the GE7 conjugate significantly less.

     

Machine Learning Methods in Drug Discovery

The advancements of information technology and related processing techniques have created a fertile base for progress in many scientific fields and industries. In the fields of drug discovery and development, machine learning techniques have been used for the development of novel drug candidates. The methods for designing drug targets and novel drug discovery now routinely combine machine learning and deep learning algorithms to enhance the efficiency, efficacy, and quality of developed outputs. The generation and incorporation of big data, through technologies such as high-throughput screening and high through-put computational analysis of databases used for both lead and target discovery, has increased the reliability of the machine learning and deep learning incorporated techniques. The use of these virtual screening and encompassing online information has also been highlighted in developing lead synthesis pathways. In this review, machine learning and deep learning algorithms utilized in drug discovery and associated techniques will be discussed. The applications that produce promising results and methods will be reviewed.     

白蛋白作为药物载体的研究

白蛋白作为药物载体的应用已越来越广泛,其载药方式主要有两种:一是使药物和白蛋白载体间产生分子链接形成白蛋白化药物,即化学偶联的白蛋白载药;二是依赖蛋白与药物的相互作用将药物包埋于白蛋白纳米颗粒中,即物理结合的白蛋白载药。化学偶联白蛋白可改善药物的药代动力学特性,其中又可分为外源性白蛋白与药物耦合、前体药物进入体内与内源性白蛋白结合、蛋白及多肽类药物的白蛋白化。物理结合则可优化药物某些体外特性,如提高溶解性、稳定性等。基于临床应用的需求,作为载体的白蛋白正历经着修饰及改性的研究热潮。本文总结了近年来白蛋白载药技术的发展及白蛋白的修饰改性现状。     

生物催化与新药研究和开发

生物催化具有条件温和、效率高、选择性强、副产物少等特点,因此生物催化在新药的研究和开发中得到广泛的应用;此外,利用生物技术对生物催化剂进行改造优化,将使其能够更好地应用于新颖的生产工艺。本文对此领域的进展作一简单介绍和概述。     

Proteomics and Its Application in Biomarker Discovery and Drug Development

Proteomics is a research field aiming to characterize molecular and cellular dynamics in protein expression and function on a global level. The introduction of proteomics has been greatly broadening our view and accelerating our path in various medical researches. The most significant advantage of proteomics is its ability to examine a whole proteome or sub-proteome in a single experiment so that the protein alterations corresponding to a pathological or biochemical condition at a given time c…     

Novel Big Data-Driven Machine Learning Models for Drug Discovery Application

Most contemporary drug discovery projects start with a ‘hit discovery’ phase where small chemicals are identified that have the capacity to interact, in a chemical sense, with a protein target involved in a given disease. To assist and accelerate this initial drug discovery process, ’virtual docking calculations’ are routinely performed, where computational models of proteins and computational models of small chemicals are evaluated for their capacities to bind together. In cutting-edge, contemporary implementations of this process, several conformations of protein targets are independently assayed in parallel ‘ensemble docking’ calculations. Some of these protein conformations, a minority of them, will be capable of binding many chemicals, while other protein conformations, the majority of them, will not be able to do so. This fact that only some of the conformations accessible to a protein will be ’selected’ by chemicals is known as ’conformational selection’ process in biology. This work describes a machine learning approach to characterize and identify the properties of protein conformations that will be selected (i.e., bind to) chemicals, and classified as potential binding drug candidates, unlike the remaining non-binding drug candidate protein conformations. This work also addresses the class imbalance problem through advanced machine learning techniques that maximize the prediction rate of potential protein molecular conformations for the test case proteins ADORA2A (Adenosine A2a Receptor) and OPRK1 (Opioid Receptor Kappa 1), and subsequently reduces the failure rates and hastens the drug discovery process.     

Open-Source Browser-Based Tools for Structure-Based Computer-Aided Drug Discovery

We here outline the importance of open-source, accessible tools for computer-aided drug discovery (CADD). We begin with a discussion of drug discovery in general to provide context for a subsequent discussion of structure-based CADD applied to small-molecule ligand discovery. Next, we identify usability challenges common to many open-source CADD tools. To address these challenges, we propose a browser-based approach to CADD tool deployment in which CADD calculations run in modern web browsers on users’ local computers. The browser app approach eliminates the need for user-initiated download and installation, ensures broad operating system compatibility, enables easy updates, and provides a user-friendly graphical user interface. Unlike server apps—which run calculations “in the cloud” rather than on users’ local computers—browser apps do not require users to upload proprietary information to a third-party (remote) server. They also eliminate the need for the difficult-to-maintain computer infrastructure required to run user-initiated calculations remotely. We conclude by describing some CADD browser apps developed in our lab, which illustrate the utility of this approach. Aside from introducing readers to these specific tools, we are hopeful that this review highlights the need for additional browser-compatible, user-friendly CADD software.     

Antibody–Drug Conjugates: An Emerging Concept in Cancer Therapy

Traditional cancer chemotherapy is often accompanied by systemic toxicity to the patient. Monoclonal antibodies against antigens on cancer cells offer an alternative tumor‐selective treatment approach. However, most monoclonal antibodies are not sufficiently potent to be therapeutically active on their own. Antibody–drug conjugates (ADCs) use antibodies to deliver a potent cytotoxic compound selectively to tumor cells, thus improving the therapeutic index of chemotherapeutic agents. The recent approval of two ADCs, brentuximab vedotin and ado‐trastuzumab emtansine, for cancer treatment has spurred tremendous research interest in this field. This Review touches upon the early efforts in the field, and describes how the lessons learned from the first‐generation ADCs have led to improvements in every aspect of this technology, i.e., the antibody, the cytotoxic compound, and the linker connecting them, leading to the current successes. The design of ADCs currently in clinical development, and results from mechanistic studies and preclinical and clinical evaluation are discussed. Emerging technologies that seek to further advance this exciting area of research are also discussed.     

Modified Curcumins as Potential Drug Candidates for Breast Cancer: An Overview

Breast cancer (BC), the most common malignancy in women, results from significant alterations in genetic and epigenetic mechanisms that alter multiple signaling pathways in growth and malignant progression, leading to limited long-term survival. Current studies with numerous drug therapies have shown that BC is a complex disease with tumor heterogeneity, rapidity, and dynamics of the tumor microenvironment that result in resistance to existing therapy. Targeting a single cell-signaling pathway is unlikely to treat or prevent BC. Curcumin (a natural yellow pigment), the principal ingredient in the spice turmeric, is well-documented for its diverse pharmacological properties including anti-cancer activity. However, its clinical application has been limited because of its low solubility, stability, and bioavailability. To overcome the limitation of curcumin, several modified curcumin conjugates and curcumin mimics were developed and studied for their anti-cancer properties. In this review, we have focused on the application of curcumin mimics and their conjugates for breast cancer.