Hypothetical Drugs Research Articles

Is the GFR-based scaling approach adequate for predicting pediatric renal clearance of drugs with passive tubular reabsorption? Insights from PBPK modeling.

Empirical maturation models (e.g., Johnson and Rhodin models) for glomerular filtration rate (GFR) are commonly used as scaling factors for predicting pediatric renal clearance, but their predictive performance for drugs featured with tubular reabsorption is poorly understood. This study investigated the adequacy of GFR-based scaling models for predicting pediatric renal clearance in drugs with passive tubular reabsorption by comparing with a mechanistic kidney model (Mech-KiM) that encompasses the physiological processes of glomerular filtration, tubular secretion, and reabsorption. The analysis utilized hypothetical drugs with varying fractions of tubular reabsorption (Freabs), alongside the model drug metronidazole, which has a Freabs of 96%. Our simulations showed that when Freabs is ≤70%, the discrepancies between the GFR-based scaling methods and the Mech-KiM model in predicting pediatric renal clearance were generally within a twofold range throughout childhood. However, for drugs with substantial tubular reabsorption (e.g., Freabs > 70%), discrepancies greater than twofold were observed between the GFR-based scaling methods and the Mech-KiM model in predicting renal clearance for young children. In neonates, the differences ranged from 5- to 10-fold when the adult Freabs was 95%. Pediatric physiologically based pharmacokinetic (PBPK) modeling of metronidazole revealed that using a GFR-based scaling method (Johnson model) significantly overestimated drug concentrations in children under 2 months, whereas utilizing the Mech-KiM model for renal clearance predictions yielded estimates closely aligned with observed concentrations. Our study demonstrates that using GFR-based scaling models to predict pediatric renal clearance might be inadequate for drugs with extensive passive tubular reabsorption (e.g., Freabs > 70%).

Abstract 4761: Understanding three-drug combinations: optimizing scheduling while avoiding toxicity

Abstract The promise of combination therapies in managing tumor resistance is constrained by the therapeutic window (the difference between efficacious and toxic doses). We have sought to understand the impact of dose scheduling on the emergence of resistance, using a mathematical model-based approach linking pharmacokinetics (PK) to a parsimonious evolutionary model of tumor growth incorporating the emergence of drug resistance. Here, we analyze the potential of a three-drug combination to minimize toxicity while preventing the emergence of resistance. We simulated eight tumor subpopulations representing resistance to one, two or three hypothetical drugs, using an exponential growth model with growth penalties based on drug concentrations. We used the model to simulate the effects of pairs of antagonistic, synergistic, and additive drugs on the growth of sensitive and resistant populations in a tumor. Final tumor volume and resistant population were used to compare the efficacy of various drug schedules. We assumed an overlapping neutropenia-like toxicity as the dose-limiting toxicity for the three drug combination, which we have previously shown to be proportional to the peak moving average drug concentration over 18 days. Final tumor volume and peak toxicity were used to compare the effectiveness of various dosing strategies and interaction combinations. Synchronous dosing results in the greatest drug interaction effect between any two drugs. When all interactions between the three drugs are either antagonistic or synergistic, the greatest effect is observed when all drugs are dosed simultaneously. The drugs are most toxic when all three are dosed simultaneously or nearly simultaneously. Our work shows that time sensitivity—that is, the magnitude of the changes in efficacy associated with small changes in schedule—varies with phase offset. Scheduling is most time sensitive when all drugs are dosed simultaneously or nearly simultaneously; dosing of two drugs simultaneously is less time sensitive. Three-drug combinations are an effective means of preventing complete resistance. While simultaneous dosing of two or more drugs confers the greatest efficacy, such simultaneous dosing results in greater peak toxicity because of higher simultaneous drug concentrations. A potential compromise is the simultaneous dosing of two of the three drugs, which results in an intermediate toxicity. Dosing of two drugs simultaneously is also less time sensitive, advantageous in a practical sense as it is more robust to patient dosing error. While the approach used here is based on hypothetical drug properties, it provides a framework that is readily applicable to real-world drug combinations, with the potential for practical insights to guide the framing of the Target Product Profile for three-drug combinations. Citation Format: Madison Stoddard, Lin Yuan, Debra Van Egeren, Andrew Chen, Dean Bottino, Arijit Chakravarty. Understanding three-drug combinations: optimizing scheduling while avoiding toxicity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4761.

Abstract 7182: Blood brain barrier masks effects of dosing strategy

Abstract The penetration of the blood brain barrier (BBB) presents a significant challenge in the design of drugs targeting brain tumors. The BBB desynchronizes drug concentrations between the body and brain, complicating the relationships between drug dose, effective concentration, and toxicity. In this work, we sought to understand the nature of these relationships in the context of dose schedule, in order to suggest minimally toxic and maximally effective strategies. We developed a two-compartment pharmacokinetic model to simulate the exchange of a drug between the body and the brain. Drug exchange was based on passive diffusion through the barrier and active transport into the brain as described by Michaelis-Menten kinetics. In the model, tumor cells exhibit binary resistance to the drug under a logistic growth model. The drug reduces the growth rate of the sensitive population while the resistant population suffers a small growth penalty because of the mutation conferring resistance. Strategy effectiveness was quantified in terms of survival time, the time until 95% of carrying capacity was reached, and toxicity was evaluated as the peak moving average over 18 days. Although the BBB complicates prediction of drug action, it simplifies drug impact by masking schedule effects. Our work suggests that drug concentrations in the brain increase with initial treatment until equilibrium between active transport, diffusion, and drug metabolism is reached. Fluctuations in drug concentration resulting from breaks between bolus dosing are greatly reduced in the brain relative to the body, and high frequency (daily) dosing results in a nearly constant equilibrium drug concentration. The rate at which equilibrium is reached and the drug concentration in the brain are unaffected by dosing period, as long as the average dose per day is constant. Our model suggests that the impact of dosing cycle length on survival time is negligible. Neutropenia is minimized by schedules with dosing cycle lengths that factor evenly into the 18-day neutrophil recovery period. These schedules have consistent toxicity throughout. Strategies with two, three, six, nine, and eighteen day cycles induce neutropenia least. As fluctuations in drug concentration are small at the equilibrium steady-state, effects of scheduling on drug interactions are predicted to be minimal. As drug concentration in the brain at equilibrium and latency to equilibrium are independent of dosing period, these parameters must be modulated through changes in the amount of drug dosed. The use of a loading dose may compensate for latency. Although this analysis is based on hypothetical drugs, it provides practical insights that are applicable across a wide range of systemically dosed small-molecule therapeutics. Further, the modeling framework derived here can be readily extended to guide the design of dosing schedules for real-world therapeutic interventions. Citation Format: Madison Stoddard, Andrew Chen, Lin Yuan, Debra Van Egeren, Arijit Chakravarty. Blood brain barrier masks effects of dosing strategy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7182.

Abstract 4310: Impact of drug combination schedules on the evolution of tumor resistance

Abstract The emergence of drug resistance during treatment is a critical limiting factor for the long-term effectiveness of anticancer therapies. The use of drug combinations limits the emergence of resistance, thereby prolonging their therapeutic utility. The effect of a drug combination is dependent both on the concentration of the individual drugs and the extent of their interaction (synergistic, additive, or antagonistic). Thus, understanding the scheduling implications for efficacy can be valuable for optimizing tumor response and time to resistance for the combination. To examine this more closely, we used a pharmacokinetic/pharmacodynamic (PK/PD) modeling approach, coupling one-compartment PK models of two hypothetical drugs based on set synchronous or asynchronous (offset) schedules with an evolutionary model of tumor clone growth. We simulated four tumor subpopulations representing binary resistance to one or both drugs, using an exponential growth model with growth penalties based on drug concentrations. We used the model to simulate the effects of pairs of antagonistic, synergistic, and additive drugs on the growth of sensitive and resistant populations in a tumor. Final tumor volume and resistant population were used to compare the efficacy of various drug schedules. As expected, the effect of a drug interaction—either synergistic or antagonistic—is greatest when dosing is synchronous, as this results in the greatest simultaneous drug concentration. The effect of a drug interaction is proportional to the sensitive population size, so the effect of drug interactions decreases over time as the sensitive population is eliminated. Intuitively, the final tumor volume decreases as the strength of a synergistic interaction increases until the sensitive population is eliminated. The resistant populations are unaffected by the strength of the interaction for both antagonistic and synergistic combinations. Resistant populations are not affected by interactions between drugs to which they are resistant, so the growth of resistant populations over time is independent of drug interactions. Crucially, for effective drugs—that is, drugs that eliminate the sensitive population—synergy and antagonism converge to additivity as the sensitive population dies out because the remaining resistant populations are not affected by drug interactions. As synergistic and additive drug combinations are equivalent in the long-term, synergy and asynchronous dosing could be used to limit toxicity without sacrificing efficacy. The evolution of resistance also explains why in vitro synergy or antagonism may not translate in vivo. Taken together, our work provides several unintuitive insights about combination drug scheduling with practical implications for therapeutic design. Citation Format: Madison Stoddard, Andrew Chen, Lin Yuan, Debra Van Egeren, Dean Bottino, Arijit Chakravarty. Impact of drug combination schedules on the evolution of tumor resistance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4310.

Power to identify exposure-response relationships in phase IIa pulmonary tuberculosis trials with multi-dimensional bacterial load modeling.

Adequate power to identify an exposure-response relationship in a phase IIa clinical trial for pulmonary tuberculosis (TB) is important for dose selection and design of follow-up studies. Currently, it is not known what response marker provides the pharmacokinetic-pharmacodynamic (PK-PD) model more power to identify an exposure-response relationship. We simulated colony-forming units (CFU) and time-to-positivity (TTP) measurements for four hypothetical drugs with different activity profiles for 14 days. The power to identify exposure-response relationships when analyzing CFU, TTP, or combined CFU + TTP data was determined at 60 total participants, or with 25 out of 60 participants in the lowest and highest dosing groups (unbalanced design). For drugs with moderate bactericidal activity, power was low (<59%), irrespective of the data analyzed. Power was 1.9% to 29.4% higher when analyzing TTP data compared to CFU data. Combined analysis of CFU and TTP further improved the power, on average by 4.2%. For a drug with a medium-high activity, the total sample size needed to achieve 80% power was 136 for CFU, 72 for TTP, and 68 for combined CFU + TTP data. The unbalanced design improved the power by 16% over the balanced design. In conclusion, the power to identify an exposure-response relationship is low for TB drugs with moderate bactericidal activity or with a slow onset of activity. TTP provides the PK-PD model with more power to identify exposure-response relationships compared to CFU, and combined analysis or an unbalanced dosing group study design offers modest further improvement.

Unraveling the Effects of Acute Inflammation on Pharmacokinetics: A Model-Based Analysis Focusing on Renal Glomerular Filtration Rate and Cytochrome P450 3A4-Mediated Metabolism.

A physiologically based model incorporating renal and hepatic drug clearance was implemented. Functions correlating IL-6 levels with GFR and in vitro CYP3A4 activity were derived and incorporated into the modeling framework. We then simulated treatment scenarios for hypothetical drugs by varying the IL-6 levels, the contribution of renal and hepatic drug clearance, and protein binding. The relative change in observed area under the concentration-timecurve (AUC) was computed for these scenarios. Inflammation showed opposite effects on drug exposure for drugs eliminated via the liver and kidney, with the effect of inflammation being inversely proportional to the extraction ratio (ER). For renally cleared drugs, the relative decrease in AUC was close to 30% during severe inflammation. For CYP3A4 substrates, the relative increase in AUC could exceed 50% for low-ER drugs. Finally, the impact of inflammation-induced changes in drug clearance is smaller for drugs with a larger unbound fraction. This analysis demonstrates differences in the impact of inflammation on drug clearance for different drug types. The effects of inflammation status on pharmacokinetics may explain the inter-individual variability in pharmacokinetics in critically ill patients. The proposed model-based analysis may be used to further evaluate the effect of inflammation, i.e., by incorporating the effect of inflammation on other drug-metabolizing enzymes or physiological processes.

Optimization of trial duration to predict long-term HbA1c change with therapy: A pharmacometrics simulation-based evaluation.

Glycated hemoglobin (HbA1c) is the main biomarker of diabetes drug development. However, because of its delayed turnover, trial duration is rarely shorter than 12 weeks, and being able to predict long-term HbA1c with precision using data from shorter studies would be beneficial. The feasibility of reducing study duration was therefore investigated in this study, assuming a model-based analysis. The aim was to investigate the predictive performance of 24- and 52-week extrapolations using data from up to 4, 6, 8 or 12 weeks, with six previously published pharmacometric models of HbA1c. Predictive performance was assessed through simulation-based dose-response predictions and model averaging (MA) with two hypothetical drugs. Results were consistent across the methods of assessment, with MA supporting the results derived from the model-based framework. The models using mean plasma glucose (MPG) or nonlinear fasting plasma glucose (FPG) effect, driving the HbA1c formation, showed good predictive performance despite a reduced study duration. The models, using the linear effect of FPG to drive the HbA1c formation, were sensitive to the limited amount of data in the shorter studies. The MA with bootstrap demonstrated strongly that a 4-week study duration is insufficient for precise predictions of all models. Our findings suggest that if data are analyzed with a pharmacometric model with MPG or FPG with a nonlinear effect to drive HbA1c formation, a study duration of 8 weeks is sufficient with maintained accuracy and precision of dose-response predictions.

2D QSAR, design, docking study and ADMET of some N-aryl derivatives concerning inhibitory activity against Alzheimer disease

BackgroundAlzheimer disease (AD) is an ailment that disturbs mainly people of old age. The fundamental remedial way to deal with AD depends on the utilization of AChEI. The design of new intense and particular AChEI is critical in drug discovery. In silico technique will be used to solve the above problem. A new method was established to discover novel agents with better biological activity against Alzheimer disease.ResultsA validated model was established in this research to predict the biological activities of some anti-Alzheimer compounds and to design new hypothetical drugs influenced with molecular properties in the derived model; ATS4i, MATS2e, SpMax7_BhS, Energy(HOMO) and Molecular Weight and showed good correlation R2 = 0.936, R2adj = 0.907, Q2cv = 0.88, LOF = 0.0154 and R2ext = 0.881. All the descriptors in the model were in good agreement with the 15 test set predicted values. Five compounds were designed using D35rm as a template with improved activity. The compounds have higher and better binding scores (− 10.1, − 9.4, − 9.3, − 9.1 and − 8.1 all in kcal/mol) than the approved drugs (Donepezil = − 7.4 kcal/mol).ConclusionAs the outcome, every one of the selected and the designed compounds is created and improved as potential anti-Alzheimer agents. Despite this, the further test examines and in vivo investigations are recommended to assess the method of the activities and other pharmacological impacts on these compounds.

Quantitative methods and modeling to assess COVID-19-interrupted in vivo pharmacokinetic bioequivalence studies with two reference batches.

The coronavirus disease 2019 (COVID‐19) has presented unprecedented challenges to the generic drug development, including interruptions in bioequivalence (BE) studies. Per guidance published by the US Food and Drug Administration (FDA) during the COVID‐19 public health emergency, any protocol changes or alternative statistical analysis plan for COVID‐19‐interrupted BE study should be accompanied with adequate justifications and not lead to biased equivalence determination. In this study, we used a modeling and simulation approach to assess the potential impact of study outcomes when two different batches of a Reference Standard (RS) were to be used in an in vivo pharmacokinetic BE study due to the RS expiration during the COVID‐19 pandemic. Simulations were performed with hypothetical drugs under two scenarios: (1) uninterrupted study using a single batch of an RS, and (2) interrupted study using two batches of an RS. The acceptability of BE outcomes was evaluated by comparing the results obtained from interrupted studies with those from uninterrupted studies. The simulation results demonstrated that using a conventional statistical approach to evaluate BE for COVID‐19‐interrupted studies may be acceptable based on the pooled data from two batches. An alternative statistical method which includes a “batch” effect to the mixed effects model may be used when a significant “batch” effect was found in interrupted four‐way crossover studies. However, such alternative method is not applicable for interrupted two‐way crossover studies. Overall, the simulated scenarios are only for demonstration purpose, the acceptability of BE outcomes for the COVID19‐interrupted studies could be case‐specific.

Influence of the statistical significance of results and spin on readers’ interpretation of the results in an abstract for a hypothetical clinical trial: a randomised trial

ObjectivesTo assess the impact on readers’ interpretation of the results reported in an abstract for a hypothetical clinical trial with (1) a statistically significant result (SSR), (2) spin, (3) both...

Chasing the Target: New Phenomena of Resistance to Novel Selective RET Inhibitors in Lung Cancer. Updated Evidence and Future Perspectives

Simple SummaryREarranged during Transfection (RET) is an emerging target for several types of cancer, including non-small cell lung cancer (NSCLC). The recent U.S. FDA approval of pralsetinib and selpercatinib raises issues regarding the emergence of secondary mutations and amplifications involved in parallel signaling pathways and receptors, liable for resistance mechanisms. The aim of this review is to explore recent knowledge on RET resistance in NSCLC in pre-clinic and in clinical settings and accordingly, the state-of-the-art in new drugs or combination of drugs development.The potent, RET-selective tyrosine kinase inhibitors (TKIs) pralsetinib and selpercatinib, are effective against the RET V804L/M gatekeeper mutants, however, adaptive mutations that cause resistance at the solvent front RET G810 residue have been found, pointing to the need for the development of the next-generation of RET-specific TKIs. Also, as seen in EGFR- and ALK-driven NSCLC, the rising of the co-occurring amplifications of KRAS and MET could represent other escaping mechanisms from direct inhibition. In this review, we summarize actual knowledge on RET fusions, focusing on those involved in NSCLC, the results of main clinical trials of approved RET-inhibition drugs, with particular attention on recent published results of selective TKIs, and finally, pre-clinical evidence regarding resistance mechanisms and suggestion on hypothetical and feasible drugs combinations and strategies viable in the near future.

Density Functional Theory (DFT) investigation and molecular docking simulation of 1, 2, 4-Triazole derivatives as potent inhibitors against a receptor (DNA gyrase)

Abstract Time consumed and expenses in discovering and synthesizing new hypothetical drugs with improved biological activity have been a major challenge toward the treatment of multi-drug resistance strain Mycobacterium tuberculosis (TB). To solve the above problem, Quantitative structure activity relationship (QSAR) is a recent approach developed to discover a novel drug with a better biological against M. Tuberculosis. The developed model was validated through internal and external validation test. Molecular docking studies was as well carried for all the studied compounds in order to show the interactions and binding modes between the ligand and the receptor (DNA gyrase). The lead compound (compound 3) with higher anti-tubercular activity was observed with prominent binding affinity of -11.8 kcal/mol. Therefore, compound 3 could serve as a template structure to designed compounds with more efficient activities. The outcome of this research is recommended for pharmaceutical and medicinal chemists to design and synthesis more potent compounds with prominent anti-tubercular activities using the model designed in this study.

The Influence of Drug Properties and Ontogeny of Transporters on Pediatric Renal Clearance through Glomerular Filtration and Active Secretion: a Simulation-Based Study

Glomerular filtration (GF) and active tubular secretion (ATS) contribute to renal drug elimination, with the latter remaining understudied across the pediatric age range. Therefore, we systematically analyzed the influence of transporter ontogeny on the relative contribution of GF and ATS to renal clearance CLR for drugs with different properties in children. A physiology-based model for CLR in adults was extrapolated to the pediatric population by including maturation functions for the system-specific parameters. This model was used to predict GF and ATS for hypothetical drugs with a range of drug-specific properties, including transporter-mediated intrinsic clearance (CLint,T) values, that are substrates for renal secretion transporters with different ontogeny patterns. To assess the impact of transporter ontogeny on ATS and total CLR, a percentage prediction difference (%PD) was calculated between the predicted CLR in the presence and absence of transporter ontogeny. The contribution of ATS to CLR ranges between 41 and 90% in children depending on fraction unbound and CLint,T values. If ontogeny of renal transporters is < 0.2 of adult values, CLR predictions are unacceptable (%PD > 50%) for the majority of drugs regardless of the pediatric age. Ignoring ontogeny patterns of secretion transporters increasing with age in children younger than 2 years results in CLR predictions that are not systematically acceptable for all hypothetical drugs (%PD > 50% for some drugs). This analysis identified for what drug-specific properties and at what ages the contribution of ATS on total pediatric CLR cannot be ignored. Drugs with these properties may be sensitive in vivo probes to investigate transporter ontogeny.

Computational modeling and ligand-based design of some novel hypothetical compound as prominent inhibitors against Mycobacterium tuberculosis

BackgroundTime consumed and expenses in discovering and synthesizing new hypothetical drugs with improved biological activity have been a major challenge toward the treatment of multi-drug-resistant strain Mycobacterium tuberculosis (TB). To solve the above problem, quantitative structure activity relationship (QSAR) is a recent approach developed to discover novel agents with better biological activity against M. tuberculosis.ResultsA validated QSAR model was developed in this study to predict the biological activities of some anti-tubercular compounds and to design new hypothetical drugs is influenced with the molecular descriptors, AATS7s, VR1_Dzi, VR1_Dzs, SpMin7_Bhe, and TDB8e, which has been validated through internal and external validation test. Prior to high anti-tubercular activity of the lead compound, compound 17 served as a template structure to design compounds with improved activity. Among the compounds designed, compounds 17i, 17j, and 17n were observed with improved anti-tubercular activities which ranges from 8.8981 to 9.0377 pBA.ConclusionThe outcome of this research is recommended for pharmaceutical and medicinal chemists to synthesis and carry out an in vivo and in vitro screening for the proposed designed compounds in order to substantiate the computational findings.

The Predictive Value of Glomerular Filtration Rate-Based Scaling of Pediatric Clearance and Doses for Drugs Eliminated by Glomerular Filtration with Varying Protein-Binding Properties.

IntroductionFor drugs eliminated by glomerular filtration (GF), clearance (CL) is determined by GF rate (GFR) and the unbound fraction of the drug. When predicting CL of GF-eliminated drugs in children, instead of physiologically based pharmacokinetic (PBPK) methods that consider changes in both GFR and protein binding, empiric bodyweight-based methods are often used. In this article, we explore the predictive value of scaling using a GFR function, and compare the results with linear and allometric scaling methods for drugs with different protein-binding properties.MethodsFirst, different GFR maturation functions were compared to identify the GFR function that would yield the most accurate GFR predictions across the pediatric age range compared with published pediatric inulin/mannitol CL values. Subsequently, the accuracy of pediatric CL scaling using this GFR maturation function was assessed and compared with PBPK CL predictions for hypothetical drugs binding, to varying extents, to serum albumin or α-acid glycoprotein across the pediatric age range. Additionally, empiric bodyweight-based methods were assessed.ResultsThe published GFR maturation functions yielded comparable maturation profiles, with the function reported by Salem et al. leading to the most accurate predictions. On the basis of this function, GFR-based scaling yields reasonably accurate (percentage prediction error ≤ 50%) pediatric CL values for all drugs, except for some drugs highly bound to AGP in neonates. Overall, this method was more accurate than linear or 0.75 allometric bodyweight-based scaling.ConclusionWhen scaling CL and dose by GFR function, maturational changes in plasma protein concentrations impact GF minimally, making this method a superior alternative to empiric bodyweight-based scaling.Electronic supplementary materialThe online version of this article (10.1007/s40262-020-00890-2) contains supplementary material, which is available to authorized users.

Adaptive guidelines for the treatment of gonorrhea to increase the effective life span of antibiotics among men who have sex with men in the United States: A mathematical modeling study.

The rise of gonococcal antimicrobial resistance highlights the need for strategies that extend the clinically useful life span of antibiotics. Because there is limited evidence to support the current practice of switching empiric first-line antibiotic when resistance exceeds 5% in the population, our objective was to compare the impact of alternative strategies on the effective life spans of antibiotics and the overall burden of gonorrhea. We developed and calibrated a mathematical model of gonorrhea transmission among men who have sex with men (MSM) in the United States. We calibrated the model to the estimated prevalence of gonorrhea, the rate of gonorrhea cases, and the proportion of cases presenting symptoms among MSM in the US. We used this model to project the effective life span of antibiotics and the number of gonorrhea cases expected under current and alternative surveillance strategies over a 50-year simulation period. We demonstrate that compared to the current practice, a strategy that uses quarterly (as opposed to yearly) surveillance estimates and incorporates both the estimated prevalence of resistance and the trend in the prevalence of resistance to determine treatment guidelines could extend the effective life span of antibiotics by 0.83 years. This is equivalent to successfully treating an additional 80.1 (95% uncertainty interval: [47.7, 111.9]) gonorrhea cases per 100,000 MSM population each year with the first-line antibiotics without worsening the burden of gonorrhea. If the annual number of isolates tested for drug susceptibility is doubled, this strategy could increase the effective life span of antibiotics by 0.94 years, which is equivalent to successfully treating an additional 91.1 (54.3, 127.3) gonorrhea cases per 100,000 MSM population each year without increasing the incidence of gonorrhea. Study limitations include that our conclusions might not be generalizable to other settings because our model describes the transmission of gonorrhea among the US MSM population, and, to better capture uncertainty in the characteristics of current and future antibiotics, we chose to model hypothetical drugs with characteristics similar to the antibiotics commonly used in gonorrhea treatment. Our results suggest that use of data from surveillance programs could be expanded to prolong the clinical effectiveness of antibiotics without increasing the burden of the disease. This highlights the importance of maintaining effective surveillance systems and the engagement of policy makers to turn surveillance findings into timely and effective decisions.

Anti-tubercular Modelling via QSAR Approach, In Silico Design and Virtual Docking Screening of Designed Hypothetical Inhibitors Against DNA gyrase Protein.

Mycobacterium tuberculosis has instigated a serious challenge toward the effective treatment of tuberculosis. The reoccurrence of the resistant strains of the disease to accessible drugs/medications has mandate for the development of more effective anti-tubercular agents with efficient activities. Time expended and costs in discovering and synthesizing new hypothetical drugs with improved biological activity have been a major challenge toward the treatment of multidrug resistance strain M. tuberculosis (TB). Meanwhile, to solve the stated problem, a new approach i.e. QSAR which establish connection between novel drugs with a better biological against M. tuberculosis is adopted. The anti-tubercular model established in this study to forecast the biological activities of some anti-tubercular compounds selected and to design new hypothetical drugs is subjective to the molecular descriptors; MATS7s, SM1_DzZ, SpMin4_Bhv, TDB3v and RDF70v. Ligand-receptor interactions between quinoline derivatives and the receptor (DNA gyrase) was carried out using molecular docking technique by employing the PyRx virtual screening software and discovery studio visualizer software. Furthermore, docking study indicates that compound 20 of the derivatives with promising biological activity has the utmost binding energy of -17.79 kcal/mol. Meanwhile, the interaction of the standard drug; isoniazid with the target enzyme was observed with the binding energy -14.6 kcal/mol which was significantly lesser than the binding energy of the ligand (compound 20). Therefore, compound 20 served as a template structure to design compounds with more efficient activities. Among the compounds designed; compounds 20p was observed with better anti-tubercular activities with more prominent binding affinities of - 24.3kcal/mol. The presumption of this research aid the medicinal chemists and pharmacist to design and synthesize a novel drug candidate against tuberculosis. Moreover, in-vitro and in-vivo test could be carried out to validate the computational results.

Theoretical investigation of a few selected compounds as potent anti-tubercular agents and molecular docking evaluation: A multi-linear regression approach

Emergence of multi-drug resistant strains of Mycobacterium tuberculosis to the available drugs has demanded for the development of more potent anti-tubercular agents with efficient pharmacological activities. Time consumed and expenses in discovering and synthesizing new drug targets with improved biological activity have been a major challenge toward the treatment of multi-drug resistance strain M. tuberculosis. To solve the above problem, Quantitative Structure Activity Relationship (QSAR) is a recent approach developed to discover a novel drug with a better biological against M. Tuberculosis. A validated QSAR model developed in this study to predict the biological activities of some anti-tubercular compounds and to design new hypothetical drugs is influenced with the molecular descriptors; AATS7s, VR1-Dzi, VR1-Dzs, SpMin7-Bhe and RDF110i. The internal validation test for the derived model was found to have correlation coefficient (R2) of 0.8875, adjusted correlation coefficient (R2adj) value of 0.8234 and leave one out cross validation coefficient (Qcv2) value of 0.8012 while the external validation test was found to have (R2test) of 0.7961 and Y-randomization Coefficient (cRp2) of 0.6832. Molecular docking shows that ligand 13 of 2,4-disubstituted quinoline derivatives have promising higher binding score of -18.8 kcal/mol compared to the recommended drugs; isoniazid -14.6 kcal/mol. The proposed QSAR model and molecular docking studies will provides valuable approach for the modification of the lead compound, designing and synthesis more potent anti-tubercular agents.

Quantum modelling and molecular docking evaluation of some selected quinoline derivatives as anti-tubercular agents

Mycobacterium tuberculosis has instigated a serious challenge toward the effective treatment of tuberculosis. The reoccurrence of the resistant strains of the disease to accessible drugs/medications has mandate for the development of more effective anti-tubercular agents with efficient activities. Time expended and costs in discovering and synthesizing new hypothetical drugs with improved biological activity have been a major challenge toward the treatment of multi-drug resistance strain M. tuberculosis (TB). Meanwhile, to solve the problem stated, a new approach i.e. QSAR which establish connection between novel drugs with a better biological against M. tuberculosis is adopted. The anti-tubercular model established in this study to forecast the biological activities of some anti-tubercular compounds selected and to design new hypothetical drugs is subjective to the molecular descriptors; AATS7s, VE2_Dzi, SpMin7-Bhe and RDF110i. The significant of the model were observed with R2 of 0.8738, R2 adj of 0.8351 Q_cvˆ2 of 0.7127 which served as criteria to substantiate the QSAR model. More also, the model significant with the QSAR external validation criterial ‘‘(R2test) of 0.7532. Ligand-receptor interactions between quinoline derivatives and the receptor (DNA gyrase) was carried out using molecular docking technique by employing the PyRx virtual screening software and discovery studio visualizer software. Furthermore, docking study indicates that compounds 10 of the derivatives with promising biological activity have the utmost binding energy of -18.8 kcal/mol. Meanwhile, the interaction of the standard drug; isoniazid with the target enzyme was observed with the binding energy -14.6 kcal/mol which was significantly lesser than the binding energy of the ligand (compound 10). This implies that ligand 10 could be used as a structural template to design better hypothetical anti-tubercular drugs with more efficient activities. The presumption of this research aid the medicinal chemists and pharmacist to design and synthesis a novel drug candidate against the tuberculosis. Moreover, in-vitro and in-vivo test could be carried out to validate the computational results.

Optimization of clinical dosing schedule to manage neutropenia: learnings from semi-mechanistic modeling simulation approach.

Neutropenia is a common side-effect of oncology drugs. We aimed to study the impact of exposure and dosing schedule on neutropenia to guide selection of dosing schedules that minimize neutropenia potential while maintaining the desired minimum concentration (Cmin) required for target engagement. Dose, frequency and PK parameters were chosen for five hypothetical drugs of various half-lives to (1) achieve same exposure with continuous dosing and evaluate impact of 4 intermittent dosing schedules; and (2) achieve same nadir for continuous and intermittent dosing and evaluate impact on % time above Cmin, a surrogate assumed to indicate target engagement. Absolute neutrophil count (ANC) profiles were simulated using Friberg model, a widely used semi-mechanistic myelosuppression model, assuming drug concentration directly reduce the proliferation rate of stem cells and progenitor cells in proliferation compartment. The correlations between different PK measures and neutropenia metrics were explored. In (1), when the same daily dose was used, intermittent schedules offered better management of ANC nadir. The reduced average drug exposure with intermittent dosing led to lower% time above Cmin. In (2), when the dose was adjusted to achieve the same nadir, drugs with moderate half-life (8-48h) showed similar % time above Cmin regardless of schedule, while continuous dosing was better for a short half-life (4h). Area under the concentration curve (AUC) was highly correlated with neutropenia. In summary, continuous dosing, with the dose selected correctly, is most effective to maintain % time above Cmin while providing similar tolerability as intermittent dosing with a higher dose. But dose interruptions could be required to manage individual toxicities. Intermittent schedules, on the other hand, allow recovery of ANC, enabling more orderly schedules.