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Artificial Intelligence in Health Combating XDR-bacteria as we approach 2050

Figure 27. LanC/NisC/SlvC peptide cyclase proteins exhibit 50% homology across oral bacteria. There are many isomers of nisin lantibiotic produced
by Streptococcus lactis: nisin-A, nisin-Z, nisin-Q, and nisin-U2. Nisin-H was isolated from Streptococcus hyointestnalis, and nisin-P from Streptococcus
gallolyticus. Nisin-O was isolated from a gut bacteria called Blautia obeum A2-162. Nisin-J was produced by a Staphylococcus species, and nisin-G was
produced by fecal bacteria such as Streptococcus uberis and Streptococcus sius 52

methods were not developed then, resulting in suboptimal
doses. At present, we are developing phytodrugs (CU1 and
NU2) from a few medicinal plants against MDR bacteria,
following a few basic points. The phytochemicals must be
present in sufficient amounts, typically constituting 30% of
the ethanol extract of bark, root, or leaves. Their potency
must be demonstrated by a 15 mm-diameter lysis zone
or higher in the LB-agar bacterial lysis zone assay using a
1:5 ratio (plant parts: solvent) for overnight extraction at
room temperature in a tightly capped plastic tube or bottle.
Furthermore, these chemicals must be easily separated by
preparative TLC and detectable either by the naked eye or
the UV-shadow technique. Such phytochemicals must be
cytocidal to at least ten MDR bacteria, initially selected
with ten different antibiotics, and be resistant to at least
six antibiotics, such as ampicillin, amoxicillin, cefotaxime, Figure 28. New research dimensions with the help of artificial intelligence
tetracycline, amikacin, linezolid, ciprofloxacin, novobiocin, technology to control extensively drug resistant and totally drug-resistant
trimoxazole, imipenem, streptomycin, chloramphenicol, bacterial infections as we approach 2050
erythromycin, azithromycin, lomofloxacin, norfloxacin,
and tigecycline. 14,15 for future applications in antibiotic development against
superbugs. 54-61
Figure 28 outlines future strategies, emphasizing
the potential for automation in drug discovery to 4. Conclusion
revolutionize the development of new antibiotics. In other
words, with the help of AI methods, we can outsmart The chemical synthesis of antibiotics remains vital, but it
XDR bacteria, which possess a myriad of mdr genes and has faced challenges in recent times due to high costs and
transposons. Together with a slow and expensive antibiotic concerns over MDR. Nevertheless, extensive research on
development pipeline, the proliferation of drug-resistant novel lantibiotics against XDR bacteria has been reported,
bacteria drives urgent interest in computational methods positioning peptide antibiotics to take center stage in the
that promise to expedite candidate drug discovery. 45.48 coming years. Our research on phytoantibiotics (CU1 and
Given the urgency of the antimicrobial resistance NU2) has gained momentum, with the United Nations
crisis, we must embrace open science best practices in recognizing the promising global benefits of such work
AI-driven antibiotic discovery to accelerate preclinical across all age groups. Our MDR-Cure extract represents
research on potent new drugs. 49-53 Ultimately, AI-driven a promising antibacterial ayurvedic medicine specifically
enhancements in drug discovery offer many opportunities tailored for skin and nail infections. In addition, while phage

Volume 1 Issue 2 (2024) 91 doi: 10.36922/aih.2284