A small-molecule inhibitor of BamA impervious to efflux and the outer membrane permeability barrier.

The development of new antimicrobial drugs is a priority to combat the increasing spread of multidrug-resistant bacteria. This development is especially problematic in gram-negative bacteria due to the outer membrane (OM) permeability barrier and multidrug efflux pumps. Therefore, we screened for compounds that target essential, nonredundant, surface-exposed processes in gram-negative bacteria. We identified a compound, MRL-494, that inhibits assembly of OM proteins (OMPs) by the ß-barrel assembly machine (BAM complex). The BAM complex contains one essential surface-exposed protein, BamA. We constructed a bamA mutagenesis library, screened for resistance to MRL-494, and identified the mutation bamAE470K BamAE470K restores OMP biogenesis in the presence of MRL-494. The mutant protein has both altered conformation and activity, suggesting it could either inhibit MRL-494 binding or allow BamA to function in the presence of MRL-494. By cellular thermal shift assay (CETSA), we determined that MRL-494 stabilizes BamA and BamAE470K from thermally induced aggregation, indicating direct or proximal binding to both BamA and BamAE470K Thus, it is the altered activity of BamAE470K responsible for resistance to MRL-494. Strikingly, MRL-494 possesses a second mechanism of action that kills gram-positive organisms. In microbes lacking an OM, MRL-494 lethally disrupts the cytoplasmic membrane. We suggest that the compound cannot disrupt the cytoplasmic membrane of gram-negative bacteria because it cannot penetrate the OM. Instead, MRL-494 inhibits OMP biogenesis from outside the OM by targeting BamA. The identification of a small molecule that inhibits OMP biogenesis at the cell surface represents a distinct class of antibacterial agents.

Nanoparticle Effects on Stress Response Pathways and Nanoparticle-Protein Interactions.

Nanoparticles (NPs) are increasingly used in a wide variety of applications and products; however, NPs may affect stress response pathways and interact with proteins in biological systems. This review article will provide an overview of the beneficial and detrimental effects of NPs on stress response pathways with a focus on NP-protein interactions. Depending upon the particular NP, experimental model system, and dose and exposure conditions, the introduction of NPs may have either positive or negative effects. Cellular processes such as the development of oxidative stress, the initiation of the inflammatory response, mitochondrial function, detoxification, and alterations to signaling pathways are all affected by the introduction of NPs. In terms of tissue-specific effects, the local microenvironment can have a profound effect on whether an NP is beneficial or harmful to cells. Interactions of NPs with metal-binding proteins (zinc, copper, iron and calcium) affect both their structure and function. This review will provide insights into the current knowledge of protein-based nanotoxicology and closely examines the targets of specific NPs.

Higher severe acute respiratory syndrome coronavirus 2 infection rate in pregnant patients.

BACKGROUND: During the early months of the coronavirus disease 2019 pandemic, risks associated with severe acute respiratory syndrome coronavirus 2 in pregnancy were uncertain. Pregnant patients can serve as a model for the success of clinical and public health responses during public health emergencies as they are typically in frequent contact with the medical system. Population-based estimates of severe acute respiratory syndrome coronavirus 2 infections in pregnancy are unknown because of incomplete ascertainment of pregnancy status or inclusion of only single centers or hospitalized cases. Whether pregnant women were protected by the public health response or through their interactions with obstetrical providers in the early months of pandemic is not clearly understood. OBJECTIVE: This study aimed to estimate the severe acute respiratory syndrome coronavirus 2 infection rate in pregnancy and to examine the disparities by race and ethnicity and English language proficiency in Washington State. STUDY DESIGN: Pregnant patients with a polymerase chain reaction-confirmed severe acute respiratory syndrome coronavirus 2 infection diagnosed between March 1, 2020, and June 30, 2020 were identified within 35 hospitals and clinics, capturing 61% of annual deliveries in Washington State. Infection rates in pregnancy were estimated overall and by Washington State Accountable Community of Health region and cross-sectionally compared with severe acute respiratory syndrome coronavirus 2 infection rates in similarly aged adults in Washington State. Race and ethnicity and language used for medical care of pregnant patients were compared with recent data from Washington State. RESULTS: A total of 240 pregnant patients with severe acute respiratory syndrome coronavirus 2 infections were identified during the study period with 70.7% from minority racial and ethnic groups. The principal findings in our study were as follows: (1) the severe acute respiratory syndrome coronavirus 2 infection rate was 13.9 per 1000 deliveries in pregnant patients (95% confidence interval, 8.3-23.2) compared with 7.3 per 1000 (95% confidence interval, 7.2-7.4) in adults aged 20 to 39 years in Washington State (rate ratio, 1.7; 95% confidence interval, 1.3-2.3); (2) the severe acute respiratory syndrome coronavirus 2 infection rate reduced to 11.3 per 1000 deliveries (95% confidence interval, 6.3-20.3) when excluding 45 cases of severe acute respiratory syndrome coronavirus disease 2 detected through asymptomatic screening (rate ratio, 1.3; 95% confidence interval, 0.96-1.9); (3) the proportion of pregnant patients in non-White racial and ethnic groups with severe acute respiratory syndrome coronavirus disease 2 infection was 2- to 4-fold higher than the race and ethnicity distribution of women in Washington State who delivered live births in 2018; and (4) the proportion of pregnant patients with severe acute respiratory syndrome coronavirus 2 infection receiving medical care in a non-English language was higher than estimates of pregnant patients receiving care with limited English proficiency in Washington State (30.4% vs 7.6%). CONCLUSION: The severe acute respiratory syndrome coronavirus 2 infection rate in pregnant people was 70% higher than similarly aged adults in Washington State, which could not be completely explained by universal screening at delivery. Pregnant patients from nearly all racial and ethnic minority groups and patients receiving medical care in a non-English language were overrepresented. Pregnant women were not protected from severe acute respiratory syndrome coronavirus 2 infection in the early months of the pandemic. Moreover, the greatest burden of infections occurred in nearly all racial and ethnic minority groups. These data coupled with a broader recognition that pregnancy is a risk factor for severe illness and maternal mortality strongly suggested that pregnant people should be broadly prioritized for coronavirus disease 2019 vaccine allocation in the United States similar to some states.

Disease severity, pregnancy outcomes, and maternal deaths among pregnant patients with severe acute respiratory syndrome coronavirus 2 infection in Washington State.

BACKGROUND: Evidence is accumulating that coronavirus disease 2019 increases the risk of hospitalization and mechanical ventilation in pregnant patients and for preterm delivery. However, the impact on maternal mortality and whether morbidity is differentially affected by disease severity at delivery and trimester of infection are unknown. OBJECTIVE: This study aimed to describe disease severity and outcomes of severe acute respiratory syndrome coronavirus 2 infections in pregnancy across the Washington State, including pregnancy complications and outcomes, hospitalization, and case fatality. STUDY DESIGN: Pregnant patients with a polymerase chain reaction-confirmed severe acute respiratory syndrome coronavirus 2 infection between March 1, 2020, and June 30, 2020, were identified in a multicenter retrospective cohort study from 35 sites in Washington State. Sites captured 61% of annual state deliveries. Case-fatality rates in pregnancy were compared with coronavirus disease 2019 fatality rates in similarly aged adults in Washington State using rate ratios and rate differences. Maternal and neonatal outcomes were compared by trimester of infection and disease severity at the time of delivery. RESULTS: The principal study findings were as follows: (1) among 240 pregnant patients in Washington State with severe acute respiratory syndrome coronavirus 2 infections, 1 in 11 developed severe or critical disease, 1 in 10 were hospitalized for coronavirus disease 2019, and 1 in 80 died; (2) the coronavirus disease 2019-associated hospitalization rate was 3.5-fold higher than in similarly aged adults in Washington State (10.0% vs 2.8%; rate ratio, 3.5; 95% confidence interval, 2.3-5.3); (3) pregnant patients hospitalized for a respiratory concern were more likely to have a comorbidity or underlying conditions including asthma, hypertension, type 2 diabetes mellitus, autoimmune disease, and class III obesity; (4) 3 maternal deaths (1.3%) were attributed to coronavirus disease 2019 for a maternal mortality rate of 1250 of 100,000 pregnancies (95% confidence interval, 257-3653); (5) the coronavirus disease 2019 case fatality in pregnancy was a significant 13.6-fold (95% confidence interval, 2.7-43.6) higher in pregnant patients than in similarly aged individuals in Washington State with an absolute difference in mortality rate of 1.2% (95% confidence interval, -0.3 to 2.6); and (6) preterm birth was significantly higher among women with severe or critical coronavirus disease 2019 at delivery than for women who had recovered from coronavirus disease 2019 (45.4% severe or critical coronavirus disease 2019 vs 5.2% mild coronavirus disease 2019; P<.001). CONCLUSION: Coronavirus disease 2019 hospitalization and case-fatality rates in pregnant patients were significantly higher than in similarly aged adults in Washington State. These data indicate that pregnant patients are at risk of severe or critical disease and mortality compared to nonpregnant adults, and also at risk for preterm birth.

Predictive Analytics for Care and Management of Patients With Acute Diseases: Deep Learning-Based Method to Predict Crucial Complication Phenotypes.

BACKGROUND: Acute diseases present severe complications that develop rapidly, exhibit distinct phenotypes, and have profound effects on patient outcomes. Predictive analytics can enhance physicians' care and management of patients with acute diseases by predicting crucial complication phenotypes for a timely diagnosis and treatment. However, effective phenotype predictions require several challenges to be overcome. First, patient data collected in the early stages of an acute disease (eg, clinical data and laboratory results) are less informative for predicting phenotypic outcomes. Second, patient data are temporal and heterogeneous; for example, patients receive laboratory tests at different time intervals and frequencies. Third, imbalanced distributions of patient outcomes create additional complexity for predicting complication phenotypes. OBJECTIVE: To predict crucial complication phenotypes among patients with acute diseases, we propose a novel, deep learning-based method that uses recurrent neural network-based sequence embedding to represent disease progression while considering temporal heterogeneities in patient data. Our method incorporates a latent regulator to alleviate data insufficiency constraints by accounting for the underlying mechanisms that are not observed in patient data. The proposed method also includes cost-sensitive learning to address imbalanced outcome distributions in patient data for improved predictions. METHODS: From a major health care organization in Taiwan, we obtained a sample of 10,354 electronic health records that pertained to 6545 patients with peritonitis. The proposed method projects these temporal, heterogeneous, and clinical data into a substantially reduced feature space and then incorporates a latent regulator (latent parameter matrix) to obviate data insufficiencies and account for variations in phenotypic expressions. Moreover, our method employs cost-sensitive learning to further increase the predictive performance. RESULTS: We evaluated the efficacy of the proposed method for predicting two hepatic complication phenotypes in patients with peritonitis: acute hepatic encephalopathy and hepatorenal syndrome. The following three benchmark techniques were evaluated: temporal multiple measurement case-based reasoning (MMCBR), temporal short long-term memory (T-SLTM) networks, and time fusion convolutional neural network (CNN). For acute hepatic encephalopathy predictions, our method attained an area under the curve (AUC) value of 0.82, which outperforms temporal MMCBR by 64%, T-SLTM by 26%, and time fusion CNN by 26%. For hepatorenal syndrome predictions, our method achieved an AUC value of 0.64, which is 29% better than that of temporal MMCBR (0.54). Overall, the evaluation results show that the proposed method significantly outperforms all the benchmarks, as measured by recall, F-measure, and AUC while maintaining comparable precision values. CONCLUSIONS: The proposed method learns a short-term temporal representation from patient data to predict complication phenotypes and offers greater predictive utilities than prevalent data-driven techniques. This method is generalizable and can be applied to different acute disease (illness) scenarios that are characterized by insufficient patient clinical data availability, temporal heterogeneities, and imbalanced distributions of important patient outcomes.

Clinical characteristics of 46 pregnant women with a severe acute respiratory syndrome coronavirus 2 infection in Washington State.

BACKGROUND: The impact of coronavirus disease 2019 on pregnant women is incompletely understood, but early data from case series suggest a variable course of illness from asymptomatic or mild disease to maternal death. It is unclear whether pregnant women manifest enhanced disease similar to influenza viral infection or whether specific risk factors might predispose to severe disease. OBJECTIVE: To describe maternal disease and obstetrical outcomes associated with coronavirus disease 2019 in pregnancy to rapidly inform clinical care. STUDY DESIGN: This is a retrospective study of pregnant patients with a laboratory-confirmed severe acute respiratory syndrome coronavirus 2 infection from 6 hospital systems in Washington State between Jan. 21, 2020, and April 17, 2020. Demographics, medical and obstetrical history, and coronavirus disease 2019 encounter data were abstracted from medical records. RESULTS: A total of 46 pregnant patients with a severe acute respiratory syndrome coronavirus 2 infection were identified from hospital systems capturing 40% of births in Washington State. Nearly all pregnant individuals with a severe acute respiratory syndrome coronavirus 2 infection were symptomatic (93.5%, n=43) and the majority were in their second or third trimester (43.5% [n=20] and 50.0% [n=23], respectively). Symptoms resolved in a median of 24 days (interquartile range, 13-37). Notably, 7 women were hospitalized (16%) including 1 admitted to the intensive care unit. A total of 6 cases (15%) were categorized as severe coronavirus disease 2019 with nearly all patients being either overweight or obese before pregnancy or with asthma or other comorbidities. Of the 8 deliveries that occurred during the study period, there was 1 preterm birth at 33 weeks' gestation to improve pulmonary status in a woman with class III obesity, and 1 stillbirth of unknown etiology. CONCLUSION: Severe coronavirus disease 2019 developed in approximately 15% of pregnant patients and occurred primarily in overweight or obese women with underlying conditions. Obesity and coronavirus disease 2019 may synergistically increase risk for a medically indicated preterm birth to improve maternal pulmonary status in late pregnancy. These findings support categorizing pregnant patients as a higher-risk group, particularly those with chronic comorbidities.

Predicting hepatocellular carcinoma recurrences: A data-driven multiclass classification method incorporating latent variables.

Hepatocellular carcinoma (HCC), a malignant form of cancer, is frequently treated with surgical resections, which have relatively high recurrence rates. Effective recurrence predictions enable physicians' timely detections and adequate therapeutic measures that can greatly improve patient care and outcomes. Toward that end, predictions of early versus late HCC recurrences should be considered separately to reflect their distinct onset time horizons, clinical causes, underlying clinical etiology, and pathogenesis. We propose a novel Bayesian network-based method to predict different HCC recurrence outcomes by considering the respective recurrence evolution paths. Typical patient information obtained in early stages is insufficiently informative to predict recurrence outcomes accurately, due to the lack of subsequent patient progression information. Our method alleviates such information deficiency constraints by incorporating an independent latent variable, dominant recurrence type, to regulate recurrence outcome predictions (early, late, or no recurrence). We use a real-world HCC data set to evaluate the proposed method, relative to three prevalent benchmark techniques. Overall, the results show that our method consistently and significantly outperforms all the benchmark techniques in terms of accuracy, precision, recall, and F-measures. For increased robustness, we use another data set to perform an out-of-sample evaluation and obtain similar results. This study thus contributes to HCC recurrence research and offers several implications for clinical practice.

A Deep Learning-Based Unsupervised Method to Impute Missing Values in Patient Records for Improved Management of Cardiovascular Patients.

Physicians increasingly depend on electronic health records (EHRs) to manage their patients. However, many patient records have substantial missing values that pose a fundamental challenge to their clinical use. To address this prevailing challenge, we propose an unsupervised deep learning-based method that can facilitate physicians' use of EHRs to improve their management of cardiovascular patients. By building on the deep autoencoder framework, we develop a novel method to impute missing values in patient records. To demonstrate its clinical applicability and values, we use data from cardiovascular patients and evaluate the proposed method's imputation effectiveness and predictive efficacy, in comparison with six prevalent benchmark techniques. The proposed method can impute missing values and predict important patient outcomes more effectively than all the benchmark techniques. This study reinforces the importance of adequately addressing missing values in patient records. It further illustrates how effective imputations can enable greater predictive efficacy with regard to important patient outcomes, which are crucial to the use of EHRs and health analytics for improved patient management. Supported by the complete data imputed by the proposed method, physicians can make timely patient outcome estimations (predictions) and therapeutic treatment assessments.

Chemiluminescent Protease Probe for Rapid, Sensitive, and Inexpensive Detection of Live Mycobacterium tuberculosis.

Tuberculosis (TB) is a top-ten cause of death worldwide. Successful treatment is often limited by insufficient diagnostic capabilities, especially at the point of care in low-resource settings. The ideal diagnostic must be fast, be cheap, and require minimal clinical resources while providing high sensitivity, selectivity, and the ability to differentiate live from dead bacteria. We describe here the development of a fast, luminescent, and affordable sensor of Hip1 (FLASH) for detecting and monitoring drug susceptibility of Mycobacterium tuberculosis (Mtb). FLASH is a selective chemiluminescent substrate for the Mtb protease Hip1 that, when processed, produces visible light that can be measured with a high signal-to-noise ratio using inexpensive sensors. FLASH is sensitive to fmol of recombinant Hip1 enzyme in vitro and can detect as few as thousands of Mtb cells in culture or in human sputum samples within minutes. The probe is highly selective for Mtb compared to other nontuberculous mycobacteria and can distinguish live from dead cells. Importantly, FLASH can be used to measure antibiotic killing of Mtb in culture with greatly accelerated timelines compared to traditional protocols. Overall, FLASH has the potential to enhance both TB diagnostics and drug resistance monitoring in resource-limited settings.

Cellular-Enabled Glucometers and Maternal Glucose Control: A Quality Improvement Initiative.

BACKGROUND: Management of diabetes in pregnancy is burdensome due to self-glucose monitoring, recording, and reporting demands. Cellular-enabled glucometers provide real-time transmission of glucose values independent of internet access and cell phone data plans. We describe a quality improvement (QI) intervention that introduced cellular-enabled glucometers for use during pregnancies complicated by diabetes. METHODS: Our aim was to improve maternal glucose control in a cohort of insulin-requiring pregnant women enrolled in a telemedicine diabetes program. During initial establishment of a QI program, women were offered cellular-enabled glucometers but could elect to keep their standard meter. The primary outcome evaluated was glycosylated hemoglobin A1c (HbA1c) at delivery. RESULTS: Baseline characteristics including initial HbA1c were similar between women using a standard glucometer (n = 45) and those using a cellular-enabled glucometer (n = 72). Women who used a cellular-enabled glucometer had a lower HbA1c at delivery compared to those using a standard glucometer (5.8% vs 6.3%, P = .03). This improvement was particularly notable for women with poor glucose control (defined as HbA1c >6.5%) at initial obstetric visit. Women with poor glucose control who used a cellular-enabled glucose monitor had significantly lower HbA1c at delivery (6.0% vs 6.8%, P = .03) and greater change from initial visit compared to those using a standard glucometer (-2.6% vs -1.4%, P = .02). No statistically significant differences were detected in tracked neonatal outcomes. CONCLUSION: For pregnancies complicated by insulin-requiring diabetes, use of cellular-enabled glucometers as part of a perinatal diabetes program improves glucose control at delivery with timely transmission of accurate values throughout gestation.

Adherence to self-glucose monitoring recommendations and perinatal outcomes in pregnancies complicated by diabetes mellitus.

BACKGROUND: Self-glucose monitoring is critical for the management of diabetes mellitus in pregnancy; yet, validated reports of adherence to testing recommendations and associated perinatal outcomes are limited. OBJECTIVE: Using cloud-based, self-glucose monitoring technology, we sought to answer the following questions: (1) Are there differences in the rates of testing adherence based on type of diabetes mellitus in pregnancy? (2) Is adherence to glucose monitoring recommendations associated with perinatal outcomes in pregnancies that are complicated by diabetes mellitus? We hypothesized that adherence to glucose testing recommendations varies by type of diabetes mellitus and that increased adherence to testing recommendations would be associated with improved perinatal outcomes. STUDY DESIGN: This single-center, prospective cohort study included women with type 2 diabetes mellitus and gestational diabetes mellitus who were enrolled in a perinatal diabetes program at <29 weeks gestation between December 2015 and June 2018. All women received a cellular-enabled glucometer that uploaded glucose values to a cloud-based, Health Insurance Portability and Accountability Act-compliant platform in real time that ensured transmission of accurate glucose values. The primary outcome was adherence to self-glucose monitoring recommendations. Four glucose checks were advised daily, and percentage of adherence was calculated. Secondary outcomes were preeclampsia, cesarean delivery, large-for-gestational-age neonates, and neonatal hypoglycemia. The study was powered to detect a 10% difference in the primary outcome of adherence to advised self-glucose monitoring by diabetes mellitus type. Adjusted risk ratios and 95% confidence intervals were generated with the use of logistic regression. RESULTS: This study included 103 eligible women. Baseline characteristics differed between groups, with women with type 2 diabetes mellitus having higher initial HgbA1c and body mass index when compared with women with gestational diabetes mellitus. No differences were noted in age or parity. Adherence was calculated over 20±6 weeks for women with type 2 diabetes mellitus compared with 9±4 weeks for women with gestational diabetes mellitus. Overall adherence to glucose monitoring was significantly less for women with type 2 diabetes mellitus compared with those with gestational diabetes mellitus. Mean testing adherence rates were 51%, 66%, and 70% for type 2 diabetes mellitus, and gestational diabetes mellitus, class A1 and A2, respectively (P=.016). We found that, for every 10% increase in adherence to testing recommendations, the odds of cesarean delivery, neonatal hypoglycemia, and large-for-gestational-age fetuses decreases by 15-20%. There was no association between adherence and rates of preeclampsia. CONCLUSION: This study shows that overall adherence to testing recommendations differs by diabetes mellitus type and is associated with neonatal outcomes. Improved outcomes with higher adherence may reflect more timely medication adjustments in response to real-time glucose values. Programs aimed at improving adherence could prove beneficial.

Associations of nicotine dependence and fat distribution in Chinese male adults: a cross-sectional study in Lanxi, China.

INTRODUCTION: Previous studies have showed association between smoking and central fat distribution. However, the impact of smoking on whole body fat distribution, particularly peripheral fat distribution remains unclear. METHODS: Nicotine dependence was assessed in a total of 1264 male adults aged 18-80 years using the Fagerström Test for Nicotine Dependence (FTND). Smoking status was categorised as non-smokers, former and current smokers with very low, low/moderate, or high FTND scores. Body fat distribution was determined using the dual energy X-ray absorptiometry and anthropometric measurements. Multivariable linear regression models were applied to examine the adjusted associations between body fat distribution and smoking in all participants, and its association with FTND scores in the current smokers. RESULTS: Greater waist circumference (WC), waist-to-hip ratio (WHR), waist-to-height ratio (WHtR), trunk fat percentage (%TF), android fat percentage (%AF) and android-to-gynoid fat mass ratio (AOI); but lower legs fat percentage (%LegF), limb fat percentage (%LimbF) and gynoid fat percentage (%GF) were found in current smokers with high FTND scores compared with non-smokers. In current smokers aged 60 years or older, FTND scores had positive associations with WC, WHR, WHtR, %TF, %AF and AOI, and negative associations with %LegF, %LimbF and %GF. CONCLUSIONS: Nicotine dependence was positively associated with central fat distribution and negatively associated with peripheral fat distribution in Chinese male adults, particularly in those older or heavy smokers, and these associations were independent from body mass index, which might be due to long exposure to smoking.

Utility of volumetric assessment of cervical funneling to predict cerclage failure.

OBJECTIVE: To assess the utility of cervical funnel volume as a predictor of cerclage failure. METHODS: We performed a retrospective cohort study of pregnant women with a McDonald cerclage and sonographic evidence of cervical funneling between 1/2008 and 2/2014. Funnel volume (FV) was calculated and used as a correction factor for cervical length (CL) or cerclage height (CH). Receiver operating characteristic (ROC) curves were used to compare the predictive value of CL, CL:FV, CH and CH:FV for cerclage failure at <28 or <34 weeks. CL:FV was further stratified to the <5th, <10th and >10th percentiles and analyzed for prediction of preterm delivery. RESULTS: Subjects with cerclage failure (n = 30) delivered at a mean gestational age of 29.8 +/- 5.3 weeks compared to 38.1+/- 1.39 weeks in those without failure (n = 27; p < 0.001). ROC curves demonstrated CL:FV was the best predictor of delivery <28 weeks (AUC 0.80), while CL was the best predictor of delivery <34 weeks (AUC 0.76). Stratification of CL:FV into <5th versus >10th percentile groups was predictive of early preterm delivery (25.1 weeks versus 34 weeks, p = 0.01). CONCLUSIONS: Volumetric assessment of cervical funneling may improve prediction of cerclage failure in the mid-trimester.

Fundamentals of Clinical Pharmacology With Application for Pregnant Women.

Medication use is common in pregnancy, yet for most medications the optimal formulation and dosage have not been described specifically for pregnant women. Often, adverse effects are only discovered anecdotally or after extensive off-label use occurs. Since pharmacologic research that includes pregnant women is sparse and animal studies are often not applicable to the human fetus, providers must use knowledge of drug behavior and normal physiologic changes of pregnancy to personalize treatment for pregnant women. In this review, we present an overview of the basic concepts of clinical pharmacology: pharmacokinetics, pharmacodynamics, and pharmacogenomics. The normal physiologic changes of pregnancy are presented as a framework to understand alterations in drug behavior. A clinical vignette that addresses 4 pregnancy scenarios involving medications-preterm birth, vaccination, herpes simplex virus infection, and codeine toxicity-is provided to illustrate application of core clinical pharmacologic concepts. Discussion of relevant literature illustrates the challenges of offering individualized pharmacologic therapy in pregnancy.

Principles of Anti-infective Dosing in Pregnancy.

PURPOSE: Anti-infectives are among the most commonly prescribed medications in pregnancy. However, detailed information on the pharmacokinetics and pharmacodynamics of these medications in pregnancy is limited, leading to uncertainty among clinicians regarding the tolerability and efficacy of treatments. The purposes of this review were to highlight key physiologic changes during pregnancy that influence drug behavior, and to discuss areas of active research related to anti-infective drugs in pregnancy. METHODS: A review of literature in PubMed was performed for topics related to physiologic changes of pregnancy, postcesarean surgical site infections, vaccines in pregnancy, and intrauterine infections. The literature was reviewed and pertinent sources were utilized for this article. FINDINGS: Physiologic changes during pregnancy may impact drug disposition and efficacy. Cefazolin regimens are the current prophylactic treatment of choice for postcesarean surgical site infections. Vaccines are provided in pregnancy for both maternal and neonatal benefit. Broad-spectrum antibiotics continue to be used as first-line therapy for intrauterine infections. IMPLICATIONS: Continued efforts to broaden the knowledge base on anti-infective drug behavior in pregnancy will result in increased therapeutic options for this population.