Design, synthesis, and antimicrobial evaluation of a novel bone-targeting bisphosphonate-ciprofloxacin conjugate for the treatment of osteomyelitis biofilms

Osteomyelitis is a major problem worldwide and is devastating due to the potential for limb-threatening sequelae and mortality. Osteomyelitis pathogens are bone-attached biofilms, making antibiotic delivery challenging. Here we describe a novel osteoadsorptive bisphosphonate-ciprofloxacin conjugate (BV600022), utilizing a “target and release” chemical strategy, which demonstrated a significantly enhanced therapeutic index versus ciprofloxacin for the treatment of osteomyelitis in vivo. In vitro antimicrobial susceptibility testing of the conjugate against common osteomyelitis pathogens revealed an effective bactericidal profile and sustained release of the parent antibiotic over time. Efficacy and safety were demonstrated in an animal model of periprosthetic osteomyelitis, where a single dose of 10 mg/kg (15.6 μmol/kg) conjugate reduced the bacterial load by 99% and demonstrated nearly an order of magnitude greater activity than the parent antibiotic ciprofloxacin (30 mg/kg, 90.6 μmol/kg) given in multiple doses. Conjugates incorporating a bisphosphonate and an antibiotic for bone-targeted delivery to treat osteomyelitis biofilm pathogens constitute a promising approach to providing high bone-antimicrobial potency while minimizing systemic exposure

Real-time impedance-based monitoring of the growth and inhibition of osteomyelitis biofilm pathogen Staphylococcus aureus treated with novel bisphosphonate-fluoroquinolone antimicrobial conjugates

Osteomyelitis is a limb- and life-threatening orthopedic infection predominantly caused by Staphylococcus aureus biofilms. Bone infections are extremely challenging to treat clinically. Therefore, we have been designing, synthesizing, and testing novel antibiotic conjugates to target bone infections. This class of conjugates comprises bone-binding bisphosphonates as biochemical vectors for the delivery of antibiotic agents to bone minerals (hydroxyapatite). In the present study, we utilized a real-time impedance-based assay to study the growth of Staphylococcus aureus biofilms over time and to test the antimicrobial efficacy of our novel conjugates on the inhibition of biofilm growth in the presence and absence of hydroxyapatite. We tested early and newer generation quinolone antibiotics (ciprofloxacin, moxifloxacin, sitafloxacin, and nemonoxacin) and several bisphosphonate-conjugated versions of these antibiotics (bisphosphonate-carbamate-sitafloxacin (BCS), bisphosphonate-carbamate-nemonoxacin (BCN), etidronate-carbamate-ciprofloxacin (ECC), and etidronate-carbamate-moxifloxacin (ECX)) and found that they were able to inhibit Staphylococcus aureus biofilms in a dose-dependent manner. Among the conjugates, the greatest antimicrobial efficacy was observed for BCN with an MIC of 1.48 µg/mL. The conjugates demonstrated varying antimicrobial activity depending on the specific antibiotic used for conjugation, the type of bisphosphonate moiety, the chemical conjugation scheme, and the presence or absence of hydroxyapatite. The conjugates designed and tested in this study retained the bone-binding properties of the parent bisphosphonate moiety as confirmed using high-performance liquid chromatography. They also retained the antimicrobial activity of the parent antibiotic in the presence or absence of hydroxyapatite, albeit at lower levels due to the nature of their chemical modification. These findings will aid in the optimization and testing of this novel class of drugs for future applications to pharmacotherapy in osteomyelitis

Datasheet1_Machine learning enabled design features of antimicrobial peptides selectively targeting peri-implant disease progression.docx

Peri-implantitis is a complex infectious disease that manifests as progressive loss of alveolar bone around the dental implants and hyper-inflammation associated with microbial dysbiosis. Using antibiotics in treating peri-implantitis is controversial because of antibiotic resistance threats, the non-selective suppression of pathogens and commensals within the microbial community, and potentially serious systemic sequelae. Therefore, conventional treatment for peri-implantitis comprises mechanical debridement by nonsurgical or surgical approaches with adjunct local microbicidal agents. Consequently, current treatment options may not prevent relapses, as the pathogens either remain unaffected or quickly re-emerge after treatment. Successful mitigation of disease progression in peri-implantitis requires a specific mode of treatment capable of targeting keystone pathogens and restoring bacterial community balance toward commensal species. Antimicrobial peptides (AMPs) hold promise as alternative therapeutics through their bacterial specificity and targeted inhibitory activity. However, peptide sequence space exhibits complex relationships such as sparse vector encoding of sequences, including combinatorial and discrete functions describing peptide antimicrobial activity. In this paper, we generated a transparent Machine Learning (ML) model that identifies sequence-function relationships based on rough set theory using simple summaries of the hydropathic features of AMPs. Comparing the hydropathic features of peptides according to their differential activity for different classes of bacteria empowered predictability of antimicrobial targeting. Enriching the sequence diversity by a genetic algorithm, we generated numerous candidate AMPs designed for selectively targeting pathogens and predicted their activity using classifying rough sets. Empirical growth inhibition data is iteratively fed back into our ML training to generate new peptides, resulting in increasingly more rigorous rules for which peptides match targeted inhibition levels for specific bacterial strains. The subsequent top scoring candidates were empirically tested for their inhibition against keystone and accessory peri-implantitis pathogens as well as an oral commensal bacterium. A novel peptide, VL-13, was confirmed to be selectively active against a keystone pathogen. Considering the continually increasing number of oral implants placed each year and the complexity of the disease progression, prevalence of peri-implant diseases continues to rise. Our approach offers transparent ML-enabled paths towards developing antimicrobial peptide-based therapies targeting the changes in the microbial communities that can beneficially impact disease progression.</p