Nanotechnology is a rapidly growing field having potential applications in many areas. Nanoparticles (NPs) have been studied for cell toxicity, immunotoxicity, and genotoxicity. Tetrazolium-based assays such as MTT, MTS, and WST-1 are used to determine cell viability. Cell inflammatory response induced by NPs is checked by measuring inflammatory biomarkers, such as IL-8, IL-6, and tumor necrosis factor, using ELISA. Lactate dehydrogenase (LDH) assay is used for cell membrane integrity. Different types of cell cultures, including cancer cell lines have been employed as in vitro toxicity models. It has been generally agreed that NPs interfere with either assay materials or with detection systems. So far, toxicity data generated by employing such models are conflicting and inconsistent. Therefore, on the basis of available experimental models, it may be difficult to judge and list some of the more valuable NPs as more toxic to biological systems and vice versa. Considering the potential applications of NPs in many fields and the growing apprehensions of FDA about the toxic potential of nanoproducts, it is the need of the hour to look for new internationally agreed free of bias toxicological models by focusing more on in vivo studies.

/pdf/toxicity-of-nanoparticles-and-an-overview-of-current-2uaftnoeh6.pdf

Toxicity of Nanoparticles and an Overview of Current Experimental Models

Reducing the burden of infectious diseases that affect people in the developing world requires sustained collaborative drug discovery efforts. The quality of the chemical starting points for such projects is a key factor in improving the likelihood of clinical success, and so it is important to set clear go/no-go criteria for the progression of hit and lead compounds. With this in mind, the Japanese Global Health Innovative Technology (GHIT) Fund convened with experts from the Medicines for Malaria Venture, the Drugs for Neglected Diseases initiative and the TB Alliance, together with representatives from the Bill &Melinda Gates Foundation, to set disease-specific criteria for hits and leads for malaria, tuberculosis, visceral leishmaniasis and Chagas disease. Here, we present the agreed criteria and discuss the underlying rationale.

Hit and lead criteria in drug discovery for infectious diseases of the developing world

https://www.cell.com/trends/pharmacological-sciences/pdf/S0165-6147(13)00078-3.pdf

Targeting protein-protein interactions as an anticancer strategy

Recent advances in microscope automation provide new opportunities for high-throughput cell biology, such as image-based screening. High-complex image analysis tasks often make the implementation of static and predefined processing rules a cumbersome effort. Machine-learning methods, instead, seek to use intrinsic data structure, as well as the expert annotations of biologists to infer models that can be used to solve versatile data analysis tasks. Here, we explain how machine-learning methods work and what needs to be considered for their successful application in cell biology. We outline how microscopy images can be converted into a data representation suitable for machine learning, and then introduce various state-of-the-art machine-learning algorithms, highlighting recent applications in image-based screening. Our Commentary aims to provide the biologist with a guide to the application of machine learning to microscopy assays and we therefore include extensive discussion on how to optimize experimental workflow as well as the data analysis pipeline.

Machine learning in cell biology – teaching computers to recognize phenotypes

The rapid emergence of multidrug-resistant pathogens has evolved into a global health problem as current treatment options are failing for infections caused by pan-resistant bacteria. Hence, novel antibiotics are in high demand, and for this reason antimicrobial peptides (AMPs) have attracted considerable interest, since they often show broad-spectrum activity, fast killing and high cell selectivity. However, the therapeutic potential of natural AMPs is limited by their short plasma half-life. Antimicrobial peptidomimetics mimic the structure and biological activity of AMPs, but display extended stability in the presence of biological matrices. In the present review, focus is on the developments reported in the last decade with respect to their design, synthesis, antimicrobial activity, cytotoxic side effects as well as their potential applications as anti-infective agents. Specifically, only peptidomimetics with a modular structure of residues connected via amide linkages will be discussed. These comprise the classes of α-peptoids (N-alkylated glycine oligomers), β-peptoids (N-alkylated β-alanine oligomers), β3-peptides, α/β3-peptides, α-peptide/β-peptoid hybrids, α/γ N-acylated N-aminoethylpeptides (AApeptides), and oligoacyllysines (OAKs). Such peptidomimetics are of particular interest due to their potent antimicrobial activity, versatile design, and convenient optimization via assembly by standard solid-phase procedures.

https://www.mdpi.com/1420-3049/22/9/1430/pdf

Advances in Development of Antimicrobial Peptidomimetics as Potential Drugs

Cell Viability Assays -- Assay Guidance Manual

Setting up a drug discovery and development program sin academic, non-profit and other life science research companies require careful planning. This chapter contains guidelines to develop therapeutic hypothesis, target and pathway validation, proof of concept criteria and generalized cost analysis at various stages of early drug discovery. Various decision points in developing a New Chemical Entity (NCE), description of the exploratory IND and orphan drug designation, drug repurposing and drug delivery technologies are also described geared toward those who intend to develop new drug discovery and development programs. 1 2 3 4 5 6 7 8 9 10 11

Early Drug Discovery and Development Guidelines: For Academic Researchers, Collaborators, and Start-up Companies -- Assay Guidance Manual

Inhibition of Protein-Protein Interactions: Non-Cellular Assay Formats -- Assay Guidance Manual

HTS Assay Validation -- Assay Guidance Manual

Automated microscope based High Content Screening (HCS, or HCA, HCI) has gained significant momentum recently due to its ability to study many features simultaneously in complex biology systems. HCS can be used all along the preclinical drug discovery pipeline, it has the power to identify and validate new drug targets or new lead compounds, to predict in vivo toxicity, and to suggest pathways or molecular targets of orphan compounds. HCS also has the potential to be used to support clinical trials, such as companion diagnostics. In this chapter, state of the art HCS approaches are detailed, and challenges specific to HCS are discussed. It should serve as an introduction for new HCS practitioners. More chapters will follow on specific assay examples and on high level informatics analysis. Asay G uance M aual

Grossman A

Papers

Cell Viability Assays -- Assay Guidance Manual

Early Drug Discovery and Development Guidelines: For Academic Researchers, Collaborators, and Start-up Companies -- Assay Guidance Manual

Inhibition of Protein-Protein Interactions: Non-Cellular Assay Formats -- Assay Guidance Manual

HTS Assay Validation -- Assay Guidance Manual

Assay Development Guidelines for Image-Based High Content Screening, High Content Analysis and High Content Imaging -- Assay Guidance Manual