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Recently, using artificial intelligence (AI) in drug discovery has received much attention since it significantly shortens the time and cost of developing new drugs. Deep learning (DL)-based approaches are increasingly being used in all stages of drug development as DL technology advances, and drug-related data grows. Therefore, this paper presents a systematic Literature review (SLR) that integrates the recent DL technologies and applications in drug discovery Including, drug–target interactions (DTIs), drug–drug similarity interactions (DDIs), drug sensitivity and responsiveness, and drug-side effect predictions. We present a review of more than 300 articles between 2000 and 2022. The benchmark data sets, the databases, and the evaluation measures are also presented. In addition, this paper provides an overview of how explainable AI (XAI) supports drug discovery problems. The drug dosing optimization and success stories are discussed as well. Finally, digital twining (DT) and open issues are suggested as future research challenges for drug discovery problems. Challenges to be addressed, future research directions are identified, and an extensive bibliography is also included.

[This corrects the article DOI: 10.3389/fphar.2024.1524272.].

Continuous Renal Replacement Therapy (CRRT) is more and more widely used in patients for various indications recent years. It is still intricate for clinicians to decide a suitable empiric antimicrobial dosing for patients receiving CRRT. Inappropriate doses of antimicrobial agents may lead to treatment failure or drug resistance of pathogens. CRRT factors, patient individual conditions and drug pharmacokinetics/pharmacodynamics are the main elements effecting the antimicrobial dosing adjustment. With the development of CRRT techniques, some antimicrobial dosing recommendations in earlier studies were no longer appropriate for clinical use now. Here, we reviewed the literatures involving in new progresses of antimicrobial dosages, and complied the updated empirical dosing strategies based on CRRT modalities and effluent flow rates. The following antimicrobial agents were included for review: flucloxacillin, piperacillin/tazobactam, ceftriaxone, ceftazidime/avibactam, cefepime, ceftolozane/tazobactam, sulbactam, meropenem, imipenem, panipenem, biapenem, ertapenem, doripenem, amikacin, ciprofloxacin, levofloxacin, moxifloxacin, clindamycin, azithromycin, tigecycline, polymyxin B, colistin, vancomycin, teicoplanin, linezolid, daptomycin, sulfamethoxazole/trimethoprim, fluconazole, voriconazole, posaconzole, caspofungin, micafungin, amphotericin B, acyclovir, ganciclovir, oseltamivir, and peramivir.

Background Polymyxin B is the first-line therapy for Carbapenem-resistant organism (CRO) nosocomial pneumonia. However, clinical data for its pharmacokinetic/pharmacodynamic (PK/PD) relationship are limited. This study aimed to investigate the relationship between polymyxin B exposure and efficacy for the treatment of CRO pneumonia in critically ill patients, and to optimize the individual dosing regimens. Methods Patients treated with polymyxin B for CRO pneumonia were enrolled. Blood samples were assayed using a validated high-performance liquid chromatography-tandem mass spectrometry method. Population PK analysis and Monte Carlo simulation were performed using Phoenix NLME software. Logistic regression analyses and receiver operating characteristic (ROC) curve were employed to identify the significant predictors and PK/PD indices of polymyxin B efficacy. Results A total of 105 patients were included, and the population PK model was developed based on 295 plasma concentrations. AUC ss,24 h /MIC (AOR = 0.97, 95% CI 0.95–0.99, p  = 0.009), daily dose (AOR = 0.98, 95% CI 0.97–0.99, p  = 0.028), and combination of inhaled polymyxin B (AOR = 0.32, 95% CI 0.11–0.94, p  = 0.039) were independent risk factors for polymyxin B efficacy. ROC curve showed that AUC ss,24 h /MIC is the most predictive PK/PD index of polymyxin B for the treatment of nosocomial pneumonia caused by CRO, and the optimal cutoff point value was 66.9 in patients receiving combination therapy with another antimicrobial. Model-based simulation suggests that the maintaining daily dose of 75 and 100 mg Q12 h could achieve ≥ 90% PTA of this clinical target at MIC values ≤ 0.5 and 1 mg/L, respectively. For patients unable to achieve the target concentration by intravenous administration, adjunctive inhalation of polymyxin B would be beneficial. Conclusions For CRO pneumonia, daily dose of 75 and 100 mg Q12 h was recommended for clinical efficacy. Inhalation of polymyxin B is beneficial for patients who cannot achieve the target concentration by intravenous administration.

To establish a population pharmacokinetic (PopPK) model of polymyxin B (PMB) in critically ill patients based on steady-state trough (C ) and peak (C ) concentrations, optimize the dosing regimen, and evaluate the consistency of 24-hour steady-state area under the concentration-time curve (AUC ) estimation between model-based and the two-point (C and C ) methods. PopPK modeling was performed using NONMEM, Monte Carlo simulations were used to optimize PMB dosing regimens. Bland-Altman analysis was used to evaluate the consistency between the two AUC estimation methods. A total of 95 patients, contributing 214 blood samples, were included and categorized into a modeling group (n = 80) and a validation group (n = 15). A one-compartment model was developed, with creatinine clearance (CrCL) and platelet count (PLT) identified as significant covariates influencing PK parameters. Simulation results indicated that when a Minimum Inhibitory Concentration (MIC) ≤ 0.5 mg·L , a probability of target attainment (PTA) ≥ 90% was achieved in all groups except for the 50 mg every 12 h (q12h) maintenance dose group. PTA decreased as CrCL increased, with slight variations observed across different PLT levels. The 75 mg and 100 mg q12h groups showed a higher proportion of AUC within the therapeutic window. Bland-Altman analysis revealed a mean bias of 12.98 mg·h·L between the two AUC estimation methods. The Kappa test (κ = 0.51, P < 0.001) and McNemar's test (P = 0.33) demonstrated moderate agreement, reflecting overall consistency with minor discrepancies in classification outcomes. The PopPK model of PMB is well-suited for critically ill patients. The 75 mg q12h and 100 mg q12h regimens are appropriate for critically ill patients, with CrCL levels guiding individualized dosing. A two-point sampling strategy can be used for routine therapeutic drug monitoring (TDM) of PMB.

To assess the pharmacokinetics and pharmacodynamics of imipenem in a retrospective cohort of hospitalized Chinese older patients. A population pharmacokinetic (PPK) model was constructed utilizing a nonlinear mixed-effects modeling approach. The final model underwent evaluation through bootstrap resampling and visual predictive checks. Additionally, a population pharmacokinetic and pharmacodynamic analysis was conducted employing Monte Carlo simulations to investigate the impact of commonly used dosing regimens (0.25 g every 6 h, 0.5 g every 6 h, 0.5 g every 8 h, 1 g every 6 h, 1 g every 8 h, and 1 g every 12 h) on the likelihood of achieving the target therapeutic outcomes. A total of 370 observations available from 142 patients were incorporated in the PPK model. A two-compartment PPK model with linear elimination best predicted the imipenem plasma concentrations, with the creatinine clearance as a significant covariate of clearance. Typical estimates for clearance, inter-compartmental clearance, central and peripheral volume were 13.1 L·h , 11.9 L·h , 11.7 L, 29.3 L, respectively. The pharmacokinetics of imipenem in elderly patients were effectively characterized by the established PPK model, which includes creatinine clearance as a key covariate. This research will enhance our understanding of imipenem elimination and support precision dosing in this patient demographic.

Objectives: Teicoplanin has been extensively used in the treatment for infections caused by gram-positive bacteria including methicillin-resistant Staphylococcus aureus (MRSA). However, current teicoplanin treatment is challenging due to relatively low and variable concentrations under standard dosage regimens. This study aimed to investigate the population pharmacokinetics (PPK) characteristics of teicoplanin in adult sepsis patients and provide recommendations for optimal teicoplanin dosing regimens. Methods: A total of 249 serum concentration samples from 59 septic patients were prospectively collected in the intensive care unit (ICU). Teicoplanin concentrations were detected, and patients’ clinical data were recorded. PPK analysis was performed using a non-linear, mixed-effect modeling approach. Monte Carlo simulations were performed to evaluate currently recommended dosing and other dosage regimens. The optimal dosing regimens were defined and compared by different pharmacokinetic/pharmacodynamic parameters, including trough concentration (C min ), the ratio of 24-h area under the concentration-time curve to the minimum inhibitory concentration (AUC 0-24 /MIC), as well as the probability of target attainment (PTA) and the cumulative fraction of response (CFR) against MRSA. Results: A two-compartment model adequately described the data. The final model parameter estimates for clearance, central compartment volume of distribution, intercompartmental clearance and peripheral compartment volume were 1.03 L/h, 20.1 L, 3.12 L/h and 101 L, respectively. Glomerular filtration rate (GFR) was the only covariate that significantly affected teicoplanin clearance. Model-based simulations revealed that 3 or 5 loading doses of 12/15 mg/kg every 12 h followed by a maintenance dose of 12/15 mg/kg every 24 h–72 h for patients with different renal functions were required to achieve a target C min of 15 mg/L and a target AUC 0-24 /MIC of 610. For MRSA infections, PTAs and CFRs were not satisfactory for simulated regimens. Prolonging the dosing interval may be easier to achieve the target AUC 0-24 /MIC than reducing the unit dose for renal insufficient patients. Conclusion: A PPK model for teicoplanin in adult septic patients was successfully developed. Model-based simulations revealed that current standard doses may result in undertherapeutic C min and AUC, and a single dose of at least 12 mg/kg may be needed. AUC 0-24 /MIC should be preferred as the PK/PD indicator of teicoplanin, if AUC estimation is unavailable, in addition to routine detection of teicoplanin C min on Day 4, follow-up therapeutic drug monitoring at steady-state is recommended.

Cephalexin is an oral cephalosporin approved for the treatment of urinary tract infections (UTIs). Data regarding the optimal dosing interval for cephalexin in UTIs, including uncomplicated UTIs (uUTI) and complicated UTIs (cUTI), remains limited. The primary objective of this study was to compare the rates of treatment failure between patients prescribed cephalexin twice daily versus four times daily for the management of uUTIs and cUTIs once discharged from the emergency department (ED). This retrospective, single-center cohort study conducted between July 31st, 2016 and July 31st, 2023, included patients who were ≥ 18 years of age, discharged from the ED with a diagnosis of UTI, prescribed cephalexin 500 mg twice or four time daily, and a urine culture positive for Escherichia coli, Klebsiella pneumoniae, or Proteus mirabilis susceptible to cefazolin. Treatment failure was defined as return to the ED or outpatient clinic with similar or worsening UTI symptoms or change in antibiotic therapy within 30 days of the initial ED visit. Sub-group analyses were performed for both uUTI and cUTIs. In total, 214 patients were included in this analysis (50.0 % in each group). Treatment failure rates between the twice daily and four times daily dosing groups were 18.7 % versus 15.0 % (P = 0.465). Treatment failure rates in those with uUTI were 14.9 % versus 8.1 % (P = 0.197) and those with cUTI were 27.3 % versus 30.3 % (P = 0.786). For patients with UTIs, there was no statistically significant difference in treatment failure rates between patients prescribed cephalexin twice daily versus four times daily. These findings suggest cephalexin dosed twice daily may be a reasonable option for the outpatient management of UTIs diagnosed in the ED, thus increasing adherence and decreasing cost without statistically compromising effectiveness.

Background: This study aimed to develop a population pharmacokinetic (PK) model that characterized the plasma concentration–time profiles of the total and unbound pamiparib, a PARP inhibitor, in glioblastoma patients and identified patient factors influencing the PK. Methods: The total and unbound pamiparib plasma concentration data were obtained from 41 glioblastoma patients receiving 60 mg of pamiparib twice daily. Nonlinear mixed-effects modeling was performed using Monolix (2024R1) to simultaneously fit the total and unbound drug plasma concentration data. The covariate model was developed by covariate screening using generalized additive modeling followed by stepwise covariate modeling. Model simulations were performed following oral doses of 10–60 mg BID. Results: The total and unbound pamiparib plasma concentration–time profiles were best described by a one-compartment model with first-order absorption and elimination. Creatinine clearance and age were the significant covariates on the apparent volume of distribution (V/F) and apparent clearance (CL/F), respectively, explaining ~22% and ~5% of IIV of V/F and CL/F. Population estimates of the absorption rate constant (Ka), V/F, CL/F, and unbound fraction for the total drug were 1.58 h−1, 44 L, 2.59 L/h, and 0.041. Model simulations suggested that doses as low as 20 mg BID may be adequate for therapeutic effects in a general patient population, assuming that a target engagement ratio (i.e., unbound Css,min/IC50) of 5 or above is sufficient for full target engagement. Conclusions: The total and unbound pamiparib plasma PK are well characterized by a linear one-compartment model, with creatinine clearance as the significant covariate on V/F. Model simulations support further clinical investigation into dose reduction to optimize the benefit-to-risk ratio of pamiparib, particularly in combination therapies.

Objectives: The pharmacokinetics (PK) of teicoplanin differs in children compared with adults. Our aim was to determine the PK of teicoplanin in an Asian pediatric population and to optimize dosage regimens. Methods: This was a retrospective PK study and all the data were collected from hospitalized children. We developed a population PK model using sparse data, and Monte Carlo simulation was used to assess the ability of standard teicoplanin regimen and other different dosage regimens. The optimal dosing regimens were defined as achieving the target trough concentration ( C min ) of 10 mg/L and pharmacokinetic/pharmacodynamic (PK/PD, [AUC 24 /MIC]) of 125 for moderate infection. For severe infection, the optimal dosing regimens were defined as achieving the target 15 mg/L and AUC 24 /MIC of 345. Results: 159 children were included and 1.5 samples/children on average were provided. Estimated clearance of teicoplanin was 0.694 L/h (0.784/L/h/70 kg) and volume of distribution was 1.39 L. Teicoplanin standard loading dose was adequate for moderate infection, while 13 mg/kg was needed for severer infection. With standard maintenance doses, both patients with moderate and severe infection failed to achieve the target C min . 12 and 16 mg/kg/day were required to achieve a C min ≥ 10 and 15 mg/L, respectively. However, standard maintenance dose was adequate to achieve AUC 24 /MIC ≥ 125 for moderate infection, and 12 mg/kg/day was needed to achieve AUC 24 /MIC ≥ 345 for severe infection. Lower weight and serum creatinine were associated with higher dose. Conclusion: Optimal doses based on the target C min were higher than that based on the PK/PD target. To achieve the C min and PK/PD targets simultaneously, a standard loading dose was adequate for moderate infection based on simulation, while dosing higher than standard doses were required in other situation. Further clinical studies with rich sampling from children is required to confirm our findings.