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382 Chemistry and Biology of Beta-Lactams
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therapy. As far as monotherapy is concerned, cephalosporins came out on top followed by tetracycline
(second), macrolides (fourth), and cotrimoxazole (seventh). As a whole, cephalosporins were ranked
lower when combined with other antibiotics when they were used together. Among the combinations that
do not include cephalosporins, quinolones plus carbapenems came out on top (eighth). As a sequential
therapy, quinolones ranked highest (tenth) when prescribed within 30 days of cephalosporin therapy.
Antibiotics have a variable impact on ESBL-GNB selection at intestinal level, depending on whether
they are being used in monotherapy or in combination and based on previous antibiotic exposure. It is
recommended that future clinical trials on antibiotic stewardship interventions be conducted in order to
investigate these ndings further.
12.8 Prediction of β-Lactam Allergy
Beta-lactam antibiotics are also among the most common drugs to be associated with allergic reactions
as well. Additionally, it is often the case that penicillin allergies are overdiagnosed. Thus, after allergy
evaluations have been conducted, less than 30% of adults and 10% of children have been found to have
an allergy to penicillin. Having been diagnosed as having a penicillin allergy can have signicant consequences both for the patient and for the public health system. When a patient has been diagnosed with a
penicillin allergy, they are likely to receive broad-spectrum antibiotics more frequently than those without a reported penicillin allergy. Taking broad-spectrum antibiotics has been shown to increase the risk
of infection with resistant microorganisms, such as methicillin-resistant S. aureus or vancomycin-resistant Enterococcus, as well as an increased risk of C. difcile infections. Additionally, the self-reported
penicillin allergy has been associated with higher costs, an increased risk of intensive care admissions,
and a higher risk of death.
According to estimates, 8% of the general population has a beta-lactam allergy, with rates up to 15%
among hospitalized patients.
only about 1% or less of the individuals in the United States who use healthcare services report that they
are allergic to cephalosporins.
penicillin allergies would prove to be truly allergic after formal testing.
signicant insight into how allergists and non-allergists alike should evaluate and treat patients who have
been reported as having an unconrmed or reported penicillin allergy because penicillin allergies have
important implications for individual and public health.
Therefore, it is vital to establish an accurate and rapid diagnosis in order to ensure correct antibiotic
use, improve the safety of the patient, and reduce the costs to the healthcare system. There are two main
methods in which a precise diagnosis can be made: skin tests (STs) and drug provocation tests (DPTs).
Both of these methods require a signicant amount of time and are not without risks. However, allergy
testing may not always be able to be conducted in cases where an urgent need for antibiotics is present. In
recent years, there has been a growing interest in the development of risk stratication classications as
a means of identifying low-risk patients labeled as allergic to penicillin, but in whom other beta-lactam
antibiotics may be used safely.
On the basis of medical history, several computer-based guidelines have been developed in order to
stratify the risk of patients who have been labeled with penicillin allergy based on their medical history.
As a result of the implementation of some of these guidelines, the use of beta-lactams has increased as a
result. Additionally, there has been an increasing interest in developing predictive models based on the
history of a patient, in an attempt to avoid high-risk procedures such as DPTs, in recent years. Among the
most commonly used methods, logistic regression (LR) and decision trees have been the most popular.
Despite this, the performance of these methods does not seem to be acceptable in terms of predicting betalactam allergy with a high degree of accuracy. Additionally, there are other potential methods that might
be able to predict beta-lactam allergy, including articial intelligence (e.g., machine learning, articial
neural networks (ANNs), or deep learning), which have already proven useful in other areas of medicine.
An articial neural network is a type of computer system that develops capabilities based on the information it receives. By learning from cases or patterns that have already been classied, these networks
90, 91
Most of the people with beta-lactam allergies are allergic to penicillin;
92, 93
It should be noted, however, that only 5% of patients with reported
94, 95
Recent research has provided
383Articial Intelligence on Beta-Lactam Research
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are able to classify patterns correctly. In addition to their ability to adapt continuously to new patient
information, the main advantage of these models is their superior prognostic ability over standard multiple linear regression models. In multiple regression analysis, the prediction depends on the values of a
number of variables that are used to predict the outcome. As opposed to multiple regression, ANNs have
the advantage of being able to recognize nonlinear relationships between variables as well as complex
interactions between them.
There has been an evaluation of the usefulness of an ANN in the prediction of hypersensitivity to betalactam antibiotics, comparing the results of an ANN analysis with a statistical analysis of linear regression.96 The results of this study showed that the ANN was more effective than the LR when predicting
beta-lactam hypersensitivity without misdiagnosing severe allergic reactions. There is a possibility that
the ANN could be helpful in classifying the reaction risk, especially in identifying those patients with
low reaction risks, which would then be subject to open challenges to delabel them.
Using a retrospective cohort analysis, Chiriac et al. have developed predictive models for beta-lactam
allergy; however, in their subsequent study, they went so far as to perform external validation by using
a prospective dataset.97 The rst step was to develop a predictive model based on a retrospective cohort
of 1,991 allergy-referred patients who were referred to a single center in France using multivariate logistic regressions and decision trees (a type of machine learning method). In the subsequent phase, the
researchers performed external validation of selected models in 200 prospectively enrolled patients with
a history of beta-lactam allergy in three allergy centers across the country.
A common problem in the pediatric population is the overdiagnosis of beta-lactam allergies, which
is why delabeling is a crucial part of antimicrobial stewardship. By incorporating traditional delabeling strategies or newer risk-based strategies into antibiotic stewardship programs, we can mitigate
the undesirable consequences of inaccurate beta-lactam allergy labeling. A conventional approach to
assessing beta-lactam allergy relies upon a step-by-step algorithm which includes a clinical history,
skin tests, followed by DPTs. Despite this, there are a growing number of studies that point out the
suboptimal diagnostic potential of skin testing in children. It has recently been observed that there has
been a paradigm shift in the practice of assessing beta-lactam allergies due to recent challenging data
that has demonstrated the safety and accuracy of direct DPTs in children who may be experiencing
mild cutaneous reactions that are not immediate in nature such as maculopapular eruptions and delayed
urticaria as well as potentially benign immediate reactions like urticaria and angioedema. As of recent
years, identication of patients with low-risk beta-lactam allergies, where skin tests can be skipped
and direct DPTs can be safely performed, has become one of the hottest topics in the eld of allergy
research.
In recent years, new research has been conducted in the eld of drug allergy, specically in adults, to
better predict beta-lactam allergy risk status through the use of risk stratication and predictive modeling techniques. The risk assessment studies that are conducted on children are rare, in contrast to the
studies that are conducted on adults, and optimal risk denitions are also controversial. It is expected
that in the coming years, promising potential methods to elucidate the predictors of beta-lactam allergy
in children will require multidimensional approaches, including predictive analytics, AI techniques, and
point-of-care clinical decision support systems. There are new diagnostic perspectives in the management of pediatric beta-lactam allergy.
98
12.9 Conclusion
Throughout this chapter, we provided an overview of several AI-based approaches that have been utilized in beta-lactam research and discussed their advantages and disadvantages. Despite that, the continued investment in and exploration of articial intelligence in the pharmaceutical industry offer exciting
prospects for enhancing drug development processes and patient care in the future. It is due in part to the
public availability of empirical datasets, advances in the eld of computer engineering, and proliferation
of free and open-source ML libraries that the application of machine learning to drug discovery, especially antibiotic discovery, has been greatly facilitated.
384 Chemistry and Biology of Beta-Lactams
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Acknowledgments
AD is grateful to CEA-Grenoble, Joseph Fourier University, University of Göttingen, and University of
California, Los Angeles, for their support. BKB is grateful to the US NIH, the US NCI, Texas Kleberg
Foundation, Stevens Institute of Technology, University of Texas MD Anderson Cancer Center, University
of Texas-Pan American, and Community Health Systems of Texas for their nancial and moral support
to his research. AD and BKB are also grateful to their current employer, Prince Mohammad Bin Fahd
Un iversit y.
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Index
https://t.me/med1917
Absorption, distribution, metabolism, excretion, and
toxicity (ADMET), 376
Acetylation, 168
Acetyl-D-glucal, 16 6
4-Acetyloxy-N-unsubstiuted β-lactam, 85
Acid chloride, 186
Acinetobacter sp., 278, 294
A. baumannii, 326, 328, 376
Acoustic streaming, 163
Acremonium, 270
3-Acylamino-β-lactams, 67
Adlington, R. M., 63
Adverse drug reactions (ADRs), 28, 271
Agar disk diffusion method, 313
Agrobacterium, 2, 293
A. hydrophila, 12
Alcaide, B., 101, 104
Alcaligenes sp., 16
Aldimines, 338
Aldol reaction, 95
Aliphatic/aromatic amines, 338
Aliphatic ketenes, 231
Alkene-isocyanate cycloaddition method, 80 –82
4-Alkenyl β-lactam, 101
Alkyl-4-aminopyrrolidinones, 249
Alkylamino pentenoates, 249
4-Alkylidene-β-lactam derivatives, 69
3-Alkyl-ß-lactams, 98
Allene isomerization, 262, 263
3-Allyloxy/propyloxy-β-lactams, 60
Almqvist, F., 105
Alpha-glycosides, 143
6-Alpha-methoxypenicillin, 279
α-Amino acid derivatives, 253, 254
α-Aminofuranuronic acids, 253
α-Aminomethyl-γ-butyrolactones, 251, 252
α-Hydroxy acids, 258, 259
α-Hydroxy-β-lactams, 84, 344
α-Oxy-aldehyde-derived imines, 83
α,β-Epoxyaldehydes, 90
α,β-Epoxyimines, 83
5-Amino-1,10-phenanthroline, 134
2-Amino-2-(4-hydroxyphenyl) acetamido group, 345
2-Amino-(9,10)-dihydrophenanthrene, 134
Amino acids, 80, 94, 111
4-Aminobenzoic acid-derived β-lactams, 70
a-Aminobenzyl penicillin, 4
3-Amino beta-lactams, 183–185
6-Aminochrysene, 134
Aminoglycosides, 363
Aminomethylcyclopropane, 204
Aminopenicillins, 278, 345
9-Aminophenanthrene, 134
Amino-p-hydroxy-benzyl penicillin, 22
3-Aminotetrahydrofuran-2-carboxylate, 250, 251
Aminothiazoleoxime, 15
3-Amino-β-lactam derivatives, 68
Amoxicillin, 22, 279, 282, 306, 310, 316, 317
Amoxicillin–clavulanic acid, 40
Amoxyclav, 311
AmpC cephalosporinases, 272
Ampicillin, 279, 282, 308, 310, 313, 323
AMR-Diag system, 367, 369
Anaerobic bacteria, 6
Anaphylaxis, 23
Annunziata, R., 105
Anthracene group, 338, 340, 348
Antibacterial activities, of beta-lactam
carbapenems, 282–290
cephalosporins, 270 –278
abdominal infections, 271–273
ceftolozane structure activity, 275, 276
core structure, 270, 271
monocyclic β-lactams, 290–295
penicillins, 278–282
chemical structure, 278
drug combinations, 279, 281
E. coli resistance, 279, 282
mechanism of action, 279, 282
time-kill assays, 279, 280
two-component regulatory system, 279, 281
vitro kinetic model, 279
Antibacterial activity, 58, 63, 68
Antibacterial effects
with inorganic nanoparticles, 305
copper and copper oxide, 308–313
gold, 313 –317
magnetic/iron oxide, 321–322
silver, 305–308
zinc oxide, 317–321
with organic nanoparticles, 322
chitosan derivative, 323–329
cyclodextrin (CD), 329
lignin and lignin nanoparticles, 329
Antibiotic oxacillin, 20
Antibiotic pirazmonam, 14
Antibiotic resistance gene (ARG), 366
Antibiotics, 1
crucial beta-lactams, 163–164
thiena myci n β-lactam, 167–169
Anti-human cytomegalovirus (HCMV) activity, 63
Antihyperlipidemic, 290
Antimicrobial peptides (AMPs), 364
Antimicrobial resistance (AMR), 314, 322, 363
Antipseudomonal antibiotics, 23
Antipseudomonal penicillins, 278
389
390 Index
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Antistaphylococcal penicillins, 278
Aoyama, H., 63
Apoptosis, 132
Aqueous ammonia, 4
Aqueous pyridine, 4
Aqueous sodium hydroxide, 4
Area under the curve (AUC), 367
Arndtsen, B.A., 82
Aromatic hydroxylation, 64
Aromatic imines, 231
Articial intelligence dr ug design (AIDD), 373
Articial intelligence (AI) technology, 364
Articial neural network (ANN), 380, 382, 383
4-Ary l-3-c hloro -N-(3 ,4,5- trihy droxy benza mido)
-2-azetidinones, 58, 59
5-Arylimino-pyrrolidin-2-ones, 259
3-Arylsulfonyl-β-lactam, 60
Atomic force microscopy (AFM) analysis, 317
Austin Model (AM1) method, 339, 341, 348
Avi bact am, 278
7-Azabicyclo[4.2.1]nonene, 260
Azetidin-2-one nitrogen, 14
Azetidinones, 2, 89
2-azetidinone, 1, 55, 58, 63, 66, 80
2-azetidinone-tethered imines, 104
nitrogen, 89
Azidoketenes, 97
Aziridine derivatives, 258
2,3-Aziridino-γ-lactones, 247, 248
Azithromycin, 316 , 318, 363
Azlocillin, 27
Azoles derivatives, 345
Aztreonam (AZM), 5, 293, 363
Bacampicillin, 23, 24
Bacillus sp, 316
Bacterial cell wall synthesis, 284
Bacteriocins, 364
Bacteroides, 12
Bagherwal, A., 58
Balogh D. W., 90
Banik, B.K., 111, 113
Base-induced method, 111
Bayesian decision theory, 366
Beecham, 25
Benzaldimines, 228
1,2-Benzofused carbacephems, 196
Benzyl-2,3-dioxobutyrate, 106
Benzylamine, 84
Benzylazetidin-2-one analogue, 63
Benzylic oxidation, 64
Benzylic polyaromatic amines, 134
Benzylic polyaromatic compounds, 134
3-Benzylideneamino-β-lactams, 96
Benzyloxy ketene, 84
Benzyloxy triphenyl γ-lactam, 257
Benzylpenicillin (penicillin G), 18, 278, 345
4-Benzyl-β-lactam, 97
Bergman cyclization, 262
14 β-Lactam derivatives, 66
6-Beta-amidino side chain, 279
Beta-lactam-3 position, 15
Beta-lactam antibiotics (BLAs), 269, 273, 304, 363–364
antimicrobial resistance combation, 364–373
articial intelligence in drug discovery, 373–377
class, 269, 270, 363
complexes with cyclodextrins, 377–379
identication and quantitation, 373
inhibitors, 364
mechanism of action, 269–270
morphological deconvolution of polyspecicity in E.
coli, 379
oxacillin, 21
β-lactam allergy prediction, 382–383
β-lactamase proteins identication, 379–382
Beta-lactamase inhibitors (BLIs), 269, 273, 278
Beta-lactamase-resistant penicillin, 25
Beta-lactamases (β-Lases), 1, 323
Beta-lactams
acetoxy β-lactams, 150
anticancer agents, 132
asymmetric synthesis of
enzymatic approach, 14 8 –149
glycosylation of hydroxy β-lactams, 138–141
glycosylation reaction, 141–144
imines reaction and optically active acid chlorides,
144 –147
β-lactam-fused enediynes, 262 –263
bicyclic β-Lactams
carbacephem, 39– 40
carbapenem, 37–39
cephalosporins, 27–28
cephamycins, 28–35
clavulanic acid, 40– 41
oxacephem, 35–37
penicillins, 17–27
sulbactam, 40–41
tazobactam, 40–41
biological activities of, 55–56
C2–C3 bond cleavage
α-amino acid derivatives, 253, 254
α-aminofuranuronic acids, 253
C2–C3 bond formation, 80
C3–C4 bond cleavage, 254–257
1,4-diazabicyclo[4,3,0]nonanes., 256
dihyd ro-1, 4-oxa zines /pyra zine- 2,3-d iones ,
254, 255
γ-lactam, 254, 255
pyrazine-2,3-dione derivatives, 254
tetrahydroazocinones., 256
tetrahydropyridines, 255
C3–C4 bond formation, 80
C4–N1 bond cleavage, 257–260
3,4-cis-disubstituted pyrrolidines and piperidines,
260, 261
5-arylimino-pyrrolidin-2-ones., 259
7-azabicyclo[4.2.1]nonene, 260
α-hydroxy acids, 258, 259
aziridine derivatives, 258
benzyloxy triphenyl γ-lactam, 257
bicyclic pyrrolidine derivatives, 257
densely substituted γ-lactams., 259, 260
391 Index
https://t.me/med1917
glutarimides, 258
isochromans., 258, 259
C4–N1 bond formation, 80, 81
carumonam (INN), 12–14
cell cycle blockade, 149 –150
chiral preparation of, 89–118
classication of, 55, 56
computer-assisted mechanism study, 137
conformationally restricted peptides, 263–266
β-turn mimetic, 265
β-turn peptidomimetics, 265
cis- and trans-peptides, 265, 266
cyclization reactions, 57, 58
by cycloaddition reaction
chiral aldehydes, 226, 227
chiral amines, 226, 227
chiral ketenes, 227
experimental procedure, 227
optically active β-lactams, 226
racemic monocyclic β-lactams, 226
cycloaddition reactions, 57
cytotoxicity of, 137–138
diverse bond cleavage, 243, 244
hydroxamates, 14
inversion of conguration of, 169 –171
molecular lock, 262
allene isomerization, 262, 263
cyclization in enediynes, 262, 263
Enediyne-fused β-lactams, 262, 264
monocyclic β-lactams
aztreonam, 5–8
nocardicins, 3–5
tabtoxin, 7–12
tigemonam, 12
multiple bond-forming reactions
alkene-isocyanate cycloaddition method, 80 –82
carbohydrates, 83–86
catalytic chiral staudinger cycloaddition, 89
catalytic chiral Staudinger cycloaddition, 89
enolate-imine condensation, 82
optically active amines, 87–89
optically active ketenes, 86–87
Staudinger reaction, 82–83
stereochemistry differentiation, 89
N1–C2 bond cleavage, 243 –253
1,3-oxazin-6-ones, 246, 247
2,3-aziridino-γ-lactones, 247, 248
3-aminotetrahydrofuran-2-carboxylate, 250, 251
alkyl-4-aminopyrrolidinones, 249
alkylamino pentenoates, 249
α-am inomethyl-γ-butyrolactones, 251, 252
β-hydroxy amides and β-formyl amides, 245
bis-γ-lactams synthesis and formation, 244, 245
cyclic peptides, 2 51, 252
δ-lactone proline derivatives, 247
enaminones, 245, 246
ve-membered lactones Conversion, 251, 252
γ-lactam, 247, 248
indolizidines, 247, 248
oxazines, 251, 253
oxazinone nitrone, 249
piperidines, 251, 2 52
(-)-polyoxamic acid, 244
proline polypeptides, 250, 251
pyrrole derivatives, 250, 2 51
pyrrolizidines, 246
trans-tetrahydrofurans, 250
tricyclic cycloaromatized compound, 250
uracil derivatives, 247, 248
N1–C2 bond formation, 78–80
N-arylidene or alkylidene-amino-2-azetidinones,
260–262
disubstituted alkene, 262
vinyl ethers, 2 61
nucleobase chimeras, 263, 265
4-Oxo, for elastase inhibitors, 266
permeability and LogP test, 150
preparation of imines, 134
pyrrole-substituted polyaromatic β-lactams, 150 –153
ring, 2, 3, 363
scaffold, 262
signicance, 243
solid-phase synthesis of
hydroxamate approach, 209–210
modied polyethylene glycol, 212–213
resin-bound imines, 197–204
resin-bound ketenes, 195 –197
solid-phase reagents, 204–206
solid-state ionic chiral auxiliary method, 214–215
solid-supported sulfonyl-assisted synthesis,
210–212
supported rhodium nanoparticles, 213–214
three-component reactions, 204
triazene support, 210
unnatural resin-bound amino acids, 206–209
Wang resin, 213
stereochemistry differentiation, 89
stereocontrolled synthesis of, 134 –137
synthase, 40
tetracyclic β-lactams, 46
thrombin inhibitor, 106
tricyclic β-lactams, 41–46
in vitro cytotoxicity of, 149
in vivo assay against SKOV-3, 149
Bhat, I. K., 58
Biapenem, 38, 284
Bicyclic pyrrolidine derivatives, 257
Bicyclic β-lactams, 115
Biolms, 306, 311
Biotechnology methods, 2
Bis-imines, 136
Bismuth nitrate, 151
Bis-γ-lactams synthesis and formation, 244, 245
Bittermann, H., 110
B metallo-beta-lactamases, 376
Bonache, M.A., 109
Bond dipole moment, 337
Bone infections, 5
Borthwick, A. D., 60
Braun, M., 95
Broccolo, F., 69
Bromo alcohol, 168
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