The alarming increase in the incidence of Gram-positive infections, including those caused by resistant bacteria, has sparked renewed interest in novel antibiotics. One such agent is daptomycin, a novel lipopeptide antibiotic with proven bactericidal activity in vitro against all clinically relevant Gram-positive bacteria. These include resistant pathogens, such as vancomycin-resistant enterococci (VRE), methicillin-resistant Staphylococcus aureus (MRSA), glycopeptide intermediately susceptible Staphylococcus aureus (GISA), coagulase-negative staphylococci (CNS) and penicillin-resistant Streptococcus pneumoniae (PRSP), for which there are very few therapeutic alternatives. Daptomycin provides rapid, concentration-dependent killing and a relatively prolonged concentration-dependent post-antibiotic effect in vitro. Spontaneous acquisition of resistance to daptomycin occurs rarely. Daptomycin exhibits linear pharmacokinetics, minimal accumulation with once-daily dosing, and low plasma clearance and volume of distribution. Phase II clinical trials indicate that daptomycin at doses of 2 mg/kg q24 h and 3 mg/kg q12 h is efficacious against skin and soft tissue infections and bacteremia, respectively. In addition, results in endocarditis suggested potential efficacy with higher doses. On the basis of clinical trials to date, it appears that daptomycin has an excellent safety profile, with the incidence and nature of serious adverse events comparable to those observed with conventional therapy. Adverse events associated with other classes of antimicrobials (nephrotoxicity, local irritation, ototoxicity, hypersensitivity, and gastrointestinal effects) were uncommon with daptomycin. Minimal skeletal muscle toxicity was seen at only the highest dose tested (4 mg/kg q12 h), predicted by elevations in serum creatinine phosphokinase, and readily reversible upon discontinuation of treatment. There were no signs of toxicity in cardiac or smooth muscle. Phase II and III clinical trials are underway to evaluate daptomycin for the treatment of Gram-positive bacteremia and complicated skin and soft tissue infections, respectively. Daptomycin holds promise as a rapidly acting and highly effective antibiotic for Gram-positive infections.

Healthcare is undergoing a transformation, and it is imperative to leverage new technologies to generate new data and support the advent of precision medicine (PM). Recent scientific breakthroughs and technological advancements have improved our understanding of disease pathogenesis and changed the way we diagnose and treat disease leading to more precise, predictable and powerful health care that is customized for the individual patient. Genetic, genomics, and epigenetic alterations appear to be contributing to different diseases. Deep clinical phenotyping, combined with advanced molecular phenotypic profiling, enables the construction of causal network models in which a genomic region is proposed to influence the levels of transcripts, proteins, and metabolites. Phenotypic analysis bears great importance to elucidat the pathophysiology of networks at the molecular and cellular level. Digital biomarkers (BMs) can have several applications beyond clinical trials in diagnostics—to identify patients affected by a disease or to guide treatment. Digital BMs present a big opportunity to measure clinical endpoints in a remote, objective and unbiased manner. However, the use of “omics” technologies and large sample sizes have generated massive amounts of data sets, and their analyses have become a major bottleneck requiring sophisticated computational and statistical methods. With the wealth of information for different diseases and its link to intrinsic biology, the challenge is now to turn the multi-parametric taxonomic classification of a disease into better clinical decision-making by more precisely defining a disease. As a result, the big data revolution has provided an opportunity to apply artificial intelligence (AI) and machine learning algorithms to this vast data set. The advancements in digital health opportunities have also arisen numerous questions and concerns on the future of healthcare practices in particular with what regards the reliability of AI diagnostic tools, the impact on clinical practice and vulnerability of algorithms. AI, machine learning algorithms, computational biology, and digital BMs will offer an opportunity to translate new data into actionable information thus, allowing earlier diagnosis and precise treatment options. A better understanding and cohesiveness of the different components of the knowledge network is a must to fully exploit the potential of it.

Mycobacterial diseases are a major public health concern. In the case of tuberculosis, the problem has been acerbated due to the emergence of drug-resistant strains of Mycobacterium tuberculosis, and Mycobacterium avium is the major opportunistic pathogen in HIV-1 infection in the United States. M. tuberculosis and M. avium replicate in human macrophages and induce apoptosis. Incubation of freshly added uninfected autologous macrophages with apoptotic M. avium-infected macrophages results in 90% inhibition of bacterial growth. Apoptosis also prevents the release of intracellular components and the spread of mycobacterial infection by sequestering the pathogens within apoptotic bodies. Consistent with the model that host cell apoptosis is a defense mechanism against mycobacteria is the finding that the virulent M. tuberculosis strain H37Rv induces substantially less macrophage apoptosis than the attenuated strain H37Ra. Evasion of apoptosis by this pathogen is achieved by enhanced release of sTNFR2 by H37Rv-infected macrophages and subsequent formation of inactive TNF-␣-TNFR2 complexes. These observations contribute to the hypothesis that apoptosis of the host macrophage is an important defense mechanism in mycobacterial infections, which prevents the spread of the infection.

Biopharmaceuticals have the potential to raise an immunogenic response in treated individuals, which may impact the efficacy and safety profile of these drugs. As a result, it is essential to evaluate immunogenicity throughout the different phases of the clinical development of a biopharmaceutical, including post-marketing surveillance. Although rigorous evaluation of biopharmaceutical immunogenicity is required by regulatory authorities, there is a lack of uniform standards for the type, quantity, and quality of evidence, and for guidance on experimental design for immunogenicity assays or criteria to compare immunogenicity of biopharmaceuticals. Moreover, substantial technological advances in methods to assess immune responses have yielded higher immunogenicity rates with modern assays, and limit comparison of immunogenicity of biopharmaceuticals outside of head-to-head clinical trials. Accordingly, research programs, regulatory agencies, and clinicians need to keep pace with continuously evolving analyses of immunogenicity. Here, we review factors associated with immunogenicity of biopharmaceuticals, potential clinical ramifications, and current regulatory guidance for evaluating immunogenicity, and discuss methods to assess immunogenicity in non-clinical and clinical studies. We also describe special considerations for evaluating the immunogenicity of biosimilar candidates.

Virus-specific cytotoxic T lymphocytes (CTL) play a crucial role in modulating an immune response against human immunodeficiency type 1 (HIV-1) infection. The generation of effector cytotoxic cells from CTL precursors involves intricate interactions with antigen via T cell receptors (TcR) and soluble cytokines. Interleukin (IL)-7 can affect T cell maturation and differentiation. Here we report on a group of five HIV-1-positive individuals who tested negative for env- and gag-specific CTL activity. When exogenous recombinant human IL-7 was added as a stimulus to the cultures, none (0/5) of the CTL-negative individuals exhibited a CTL response. Individuals that were negative for HIV-1-specific CTL activity were found to lack IL-7 receptor (IL-7R) on CD8+ cells with a comparable reduction on CD4+ cells. Increased shedding of IL-7R in the culture supernatant was observed. A significant reduction in receptor number was detected by binding of 125I-labeled IL-7 and Scatchard analysis. The lack of IL-7R is probably not due to endogenous IL-7, since it was not detectable in the culture supernatants of the patients studied. HIV-1 proteins may cause down-modulation of IL-7R expression, either by producing an insufficient number of molecules or by rapid decay of IL-7R on T cells. These changes may alter the cells' capability to respond to the IL-7 growth signal, resulting in CTL failure and subsequent mishandling of the virus.