Drug repurposing: Iron in the fire for older drugs.

Repositioning or "repurposing" of existing therapies for indications of alternative disease is an attractive approach that can generate lower costs and require a shorter approval time than developing a de novo drug. The development of experimental drugs is time-consuming, expensive, and limited to a fairly small number of targets. The incorporation of separate and complementary data should be used, as each type of data set exposes a specific feature of organism knowledge Drug repurposing opportunities are often focused on sporadic findings or on time-consuming pre-clinical drug tests which are often not guided by hypothesis. In comparison, repurposing in-silico drugs is a new, hypothesis-driven method that takes advantage of big-data use. Nonetheless, the widespread use of omics technology, enhanced data storage, data sense, machine learning algorithms, and computational modeling all give unparalleled knowledge of the methods of action of biological processes and drugs, providing wide availability, for both disease-related data and drug-related data. This review has taken an in-depth look at the current state, possibilities, and limitations of further progress in the field of drug repositioning.

Retrospective analysis of the effect of inhaler education on improvements in inhaler usage.

INTRODUCTION: Various types of inhalation devices have been released, and it is necessary to acquire the skills for using each of them. The factors that have been previously associated with poor inhalator usage include gender, duration of disease, age, and the type of device. However, it is unclear whether these factors also apply to the Japanese population. The number of education sessions needed to acquire inhaler usage skills is also not established. PATIENTS AND METHODS: We performed a retrospective review of the medical records of selected patients and their subjective assessments of their inhaler usage skills between January 2016 and March 2018. The primary outcome was the effect of inhaler education for each inhaler device. The secondary outcomes were the factors affecting the effectiveness of inhaler education, the effects of inhalation education stratified by age, and the number of inhaler education sessions needed to improve inhaler usage skills. RESULTS: Data from 399 patients were analyzed. Age and the type of delivery device affected the mastery of inhaler usage skills. Approximately half of the patients had acquired inhaler usage skills during baseline evaluation. Approximately 90% of patients acquired inhalation usage skills after two education sessions, regardless of the type of inhalation device. Among the older patients, 35.0% had acquired inhaler usage skills during the baseline evaluation, and 86.8% acquired them after two education sessions. CONCLUSIONS: Inhaler usage skills significantly improved, regardless of the device, after inhalation education, and this was also observed in elderly patients after two education sessions.

Assessing Drug Development Risk Using Big Data and Machine Learning.

Identifying new drug targets and developing safe and effective drugs is both challenging and risky. Furthermore, characterizing drug development risk, the probability that a drug will eventually receive regulatory approval, has been notoriously hard given the complexities of drug biology and clinical trials. This inherent risk is often misunderstood and mischaracterized, leading to inefficient allocation of resources and, as a result, an overall reduction in R&D productivity. Here we argue that the recent resurgence of Machine Learning in combination with the availability of data can provide a more accurate and unbiased estimate of drug development risk.

Data Analysis-Driven Precise Asthmatic Treatment by Targeting Mast Cells.

BACKGROUND: Although the importance of mast cells in asthma has been studied, mast cellsinduced global changes in lungs are largely unknown. Data-driven identification contributes to discovering significant biomarkers or therapeutic targets, which are the basis of effective clinical medications. OBJECTIVE: This study aims to explore the effects of mast cells on gene expression in asthmatic lungs, and to assess the curative effects of inhaled budesonide (BUD). METHODS: Pulmonary gene expression in KitWsh mice with or without mast cell engraftment was analyzed with R software. Functional enrichment of Gene Ontology and KEGG was carried out through the DAVID online tool. Hub genes were identified with String and Cytoscape software. RESULTS: The array analyses showed that the mast cell engraftment enhanced inflammation/immune response, cytokine/chemokine signal, and monocyte/neutrophil/lymphocyte chemotaxis. Interleukin (IL)-6 was identified to be a significant hub gene with the highest interaction degree. Based on this, the effects of BUD were investigated on the aspects of anti-inflammation. BUD's treatment was found to reduce serum IL-6 content and pulmonary inflammation in ovalbumin-induced asthma rats. The treatment also downregulated beta-tryptase expression both in lung tissues and serum. Morphologically, the accumulation and degranulation of mast cells were significantly suppressed. Notably, the effects of BUD on inflammation and degranulation were comparable with Tranilast (a classic mast cell inhibitor), while a remarkable synergy was not observed. CONCLUSION: This study presented a unique pulmonary gene profile induced by mast cell engraftment, which could be reversed through blockage of mast cells or inhaled BUD.

An Inverse Problem Solution Scheme for Solving the Optimization Problem of Drug-Controlled Release from Multilaminated Devices.

The optimization problem of drug release based on the multilaminated drug-controlled release devices has been solved in this paper under the inverse problem solution scheme. From the viewpoint of inverse problem, the solution of optimization problem can be regarded as the solution problem of a Fredholm integral equation of first kind. The solution of the Fredholm integral equation of first kind is a well-known ill-posed problem. In order to solve the severe ill-posedness, a modified regularization method is presented based on the Tikhonov regularization method and the truncated singular value decomposition method. The convergence analysis of the modified regularization method is also given. The optimization results of the initial drug concentration distribution obtained by the modified regularization method demonstrate that the inverse problem solution scheme proposed in this paper has the advantages of the numerical accuracy and antinoise property.

A cluster randomized trial of delivery of intermittent preventive treatment of malaria in pregnancy at the community level in Burkina Faso.

BACKGROUND: Malaria in pregnancy is responsible for 8-14% of low birth weight and 20% of stillbirths in sub-Saharan Africa. To prevent these adverse consequences, the World Health Organization recommends intermittent preventive treatment of pregnant women (IPTp) with sulfadoxine-pyrimethamine be administered at each ANC visit starting as early as possible in the second trimester. Global IPTp coverage in targeted countries remains unacceptably low. Community delivery of IPTp was explored as a means to improve coverage. METHODS: A cluster randomized, controlled trial was conducted in 12 health facilities in a 1:1 ratio to either an intervention group (IPTp delivered by CHWs) or a control group (standard practice, with IPTp delivered at HFs) in three districts of Burkina Faso to assess the effect of IPTp administration by community health workers (CHWs) on the coverage of IPTp and antenatal care (ANC). The districts and facilities were purposively selected taking into account malaria epidemiology, IPTp coverage, and the presence of active CHWs. Pre- and post-intervention surveys were carried out in March 2017 and July-August 2018, respectively. A difference in differences (DiD) analysis was conducted to assess the change in coverage of IPTp and ANC over time, accounting for clustering at the health facility level. RESULTS: Altogether 374 and 360 women were included in the baseline and endline surveys, respectively. At baseline, women received a median of 2.1 doses; by endline, women received a median of 1.8 doses in the control group and 2.8 doses in the intervention group (p-value < 0.0001). There was a non-statistically significant increase in the proportion of women attending four ANC visits in the intervention compared to control group (DiD = 12.6%, p-value = 0.16). By the endline, administration of IPTp was higher in the intervention than control, with a DiD of 17.6% for IPTp3 (95% confidence interval (CI) - 16.3, 51.5; p-value 0.31) and 20.0% for IPTp4 (95% CI - 7.2, 47.3; p-value = 0.15). CONCLUSIONS: Community delivery of IPTp could potentially lead to a greater number of IPTp doses delivered, with no apparent decrease in ANC coverage.

Development of a prediction model for hypotension after induction of anesthesia using machine learning.

Arterial hypotension during the early phase of anesthesia can lead to adverse outcomes such as a prolonged postoperative stay or even death. Predicting hypotension during anesthesia induction is complicated by its diverse causes. We investigated the feasibility of developing a machine-learning model to predict postinduction hypotension. Naïve Bayes, logistic regression, random forest, and artificial neural network models were trained to predict postinduction hypotension, occurring between tracheal intubation and incision, using data for the period from between the start of anesthesia induction and immediately before tracheal intubation obtained from an anesthesia monitor, a drug administration infusion pump, an anesthesia machine, and from patients' demographics, together with preexisting disease information from electronic health records. Among 222 patients, 126 developed postinduction hypotension. The random-forest model showed the best performance, with an area under the receiver operating characteristic curve of 0.842 (95% confidence interval [CI]: 0.736-0.948). This was higher than that for the Naïve Bayes (0.778; 95% CI: 0.65-0.898), logistic regression (0.756; 95% CI: 0.630-0.881), and artificial-neural-network (0.760; 95% CI: 0.640-0.880) models. The most important features affecting the accuracy of machine-learning prediction were a patient's lowest systolic blood pressure, lowest mean blood pressure, and mean systolic blood pressure before tracheal intubation. We found that machine-learning models using data obtained from various anesthesia machines between the start of anesthesia induction and immediately before tracheal intubation can predict hypotension occurring during the period between tracheal intubation and incision.

Theoretical Modeling of Long-Time Drug Release from Nitrosalicyl-Imine-Chitosan Hydrogels through Multifractal Logistic Type Laws.

Drug release is a complex phenomenon due to the large number of interdependent side effects that occur simultaneously, involving strong nonlinear dynamics. Therefore, since their theoretical description is difficult in the classical mathematics modelling, we have built a theoretical model based on logistic type laws, validated by the correlations with the experimental data, in a special case of drug release from hydrogels. The novelty of our approach is the implementation of multifractality in logistic type laws, situation in which any chaotic system, characterized by a small number of nonlinear interactions, gets memory and, implicitly, characterization through a large number of nonlinear interactions. In other words, the complex system polymer-drug matrix becomes "pseudo-intelligent."

Statistical considerations and impact of the FDA draft guidance for assessing adhesion with transdermal delivery systems and topical patches for ANDAs.

Until 2016, a ratio of means (ROM) non-inferiority (NI) test was recommended in FDA product-specific guidances (PSGs) to evaluate adhesion performance for prospective generic transdermal delivery systems (TDS). However, the ROM NI test had low power for well-adhering TDS, which were becoming increasingly prevalent. Mathematical proof and simulation revealed that the low power wasn't because the non-normality of adhesion data violated the normality assumption of parametric methods; it was because the ROM NI test was coupled with an adhesion scale where scores approached 0 as adhesion got better. In June 2016, FDA published a draft general guidance on TDS adhesion and recommended a new statistical approach, replacing the ROM NI test with a difference-of-means (DOM) NI test, using the same scale and primary endpoint (mean adhesion scores). An analysis of 40 TDS adhesion studies submitted in ANDAs after the publication of the 2016 draft guidance suggests that, consistent with simulation results, the new statistical approach markedly improves the low power, and thereby reduces the sample size required by the old approach for moderately to well-adhering TDS, while retaining comparable power for poorly adhering TDS. The new statistical approach thus enhances the potential approvability and patient access to well-adhering generic TDS.

Low rate of intraperitoneal port placement in ovarian cancer patients, a population-based assessment.

INTRODUCTION: The National Comprehensive Cancer Network (NCCN) guidelines recommend intraperitoneal chemotherapy in optimally debulked stage III ovarian cancer patients. The objective of this investigation was to determine the rate of intraperitoneal port placement in patients undergoing surgery for ovarian cancer in a national database maintained by the American College of Surgeons. METHOD: We identified ovarian cancer patients in the National Surgical Quality Improvement Program database from 2006 to 2012. Demographics, comorbidities, operative outcomes, and postoperative complications were abstracted. Descriptive analyses were conducted using Wilcoxon rank-sum and Chi square tests, and multivariate regression models were used to analyze pre-operative and post-operative variables associated with intraperitoneal port placement. RESULTS: We identified 2659 ovarian cancer patients who underwent primary surgical management. Of these patients, only 128 (4.8%) had an intraperitoneal port placed at the time of surgery. In multivariable analyses, intraperitoneal ports were associated with body mass index ≤25, disseminated cancer, later portion of the study period (2009-2012), and operative time >200 min. Intraperitoneal port placement was not associated with any difference in surgical site infection, wound disruption, major postoperative complication, readmission within 30 days, or death within 30 days. DISCUSSION: Recent investigation of practice at NCCN institutions between 2003 and 2012 found only 35% of eligible ovarian cancer patients received intraperitoneal chemotherapy. Using intraperitoneal port placement as a surrogate for intraperitoneal chemotherapy administration, our investigation suggests an even lower rate (4.8%) nationally.

Nonadherence to Insulin Therapy Detected by Bluetooth-Enabled Pen Cap Is Associated With Poor Glycemic Control.

OBJECTIVE: To objectively evaluate adherence to timing and dosing of insulin by using Bluetooth pen caps and examine factors related to adherence. RESEARCH DESIGN AND METHODS: Bluetooth-enabled insulin pen caps were used in younger (ages 18-35 years) and older (ages ≥65 years) adults on two or more insulin injections per day. RESULTS: We evaluated 75 participants with diabetes, 42 younger (29 ± 4 years) and 33 older (73 ± 7 years). Nonadherence was found in 24% of bolus (Apidra) doses and 36% of basal (Lantus) doses. We divided participants into tertiles on the basis of overall adherence, with the most adherent tertile having 85% dose adherence compared with 49% in the least adherent tertile (P < 0.001). Participants in the most adherent tertile had better glycemic control than those in the least adherent tertile (7.7 ± 1.1% [61 ± 12 mmol/mol] vs. 8.6 ± 1.5% [70 ± 16.4 mmol/mol], P < 0.03). CONCLUSIONS: Nonadherence to insulin dosing and timing can be objectively assessed by Bluetooth pen caps and is associated with poor glycemic control.

Modeling the Effect of Mucin Binding in the Gut on Drug Delivery.

An important part the absorption, distribution, metabolism and excretion of an oral therapeutic is the flux rate of drug compound crossing the mucus lining of the gut. To understand this part of the absorption process, we develop a mathematical model of advection, diffusion and binding of drug compounds within the mucus layer of the intestines. Analysis of this model yields simple, measurable criteria for the successful mucin layer traversal of drug compound.

Oscillations-free PID control of anesthetic drug delivery in neuromuscular blockade.

BACKGROUND AND OBJECTIVES: The PID-control of drug delivery or the neuromuscular blockade (NMB) in closed-loop anesthesia is considered. The NMB system dynamics portrayed by a Wiener model can exhibit sustained nonlinear oscillations under realistic PID gains and for physiologically feasible values of the model parameters. Such oscillations, also repeatedly observed in clinical trials, lead to under- and over-dosing of the administered drug and undermine patient safety. This paper proposes a tuning policy for the proportional PID gain that via bifurcation analysis ensures oscillations-free performance of the control loop. Online estimates of the Wiener model parameters are needed for the controller implementation and monitoring of the closed-loop proximity to oscillation. METHODS: The nonlinear dynamics of the PID-controlled NMB system are studied by bifurcation analysis. A database of patient models estimated under PID-controlled neuromuscular blockade during general anesthesia is utilized, along with the corresponding clinical measurements. The performance of three recursive algorithms is compared in the application at hand: an extended Kalman filter, a conventional particle filter (PF), and a PF making use of an orthonormal basis to estimate the probability density function from the particle set. RESULTS: It is shown that with a time-varying proportional PID gain, the type of equilibria of the closed-loop system remains the same as in the case of constant controller gains. The recovery time and frequency of oscillations are also evaluated in simulation over the database of patient models. Nonlinear identification techniques based on model linearization yield biased parameter estimates and thus introduce superfluous uncertainty. The bias and variance of the estimated models are related to the computational complexity of the identification algorithms, highlighting the superiority of the PFs in this safety-critical application. CONCLUSIONS: The study demonstrates feasibility of the proposed oscillation-free control strategy combining bifurcation theory based design and online parameter estimation by PF.

A Nonlinear Mathematical Model of Drug Delivery from Polymeric Matrix.

The objective of the present study is to mathematically model the integrated kinetics of drug release in a polymeric matrix and its ensuing drug transport to the encompassing biological tissue. The model embodies drug diffusion, dissolution, solubilization, polymer degradation and dissociation/recrystallization phenomena in the polymeric matrix accompanied by diffusion, advection, reaction, internalization and specific/nonspecific binding in the biological tissue. The model is formulated through a system of nonlinear partial differential equations which are solved numerically in association with pertinent set of initial, interface and boundary conditions using suitable finite difference scheme. After spatial discretization, the system of nonlinear partial differential equations is reduced to a system of nonlinear ordinary differential equations which is subsequently solved by the fourth-order Runge-Kutta method. The model simulations deal with the comparison between a drug delivery from a biodegradable polymeric matrix and that from a biodurable polymeric matrix. Furthermore, simulated results are compared with corresponding existing experimental data to manifest the efficaciousness of the advocated model. A quantitative analysis is performed through numerical computation relied on model parameter values. The numerical results obtained reveal an estimate of the effects of biodegradable and biodurable polymeric matrices on drug release rates. Furthermore, through graphical representations, the sensitized impact of the model parameters on the drug kinetics is illustrated so as to assess the model parameters of significance.

Challenges in drug discovery targeting TriTryp diseases with an emphasis on leishmaniasis.

Tritryps diseases are devastating parasitic neglected infections caused by Leishmania spp., Trypanosoma cruzi and Trypanosoma brucei subspecies. Together, these parasites affect more than 30 million people worldwide and cause high mortality and morbidity. Leishmaniasis comprises a complex group of diseases with clinical manifestation ranging from cutaneous lesions to systemic visceral damage. Antimonials, the first-choice drugs used to treat leishmaniasis, lead to high toxicity and carry significant contraindications limiting its use. Drug-resistant parasite strains are also a matter for increasing concern, especially in areas with very limited resources. The current scenario calls for novel and/or improvement of existing therapeutics as key research priorities in the field. Although several studies have shown advances in drug discovery towards leishmaniasis in recent years, key knowledge gaps in drug discovery pipelines still need to be addressed. In this review we discuss not only scientific and non-scientific bottlenecks in drug development, but also the central role of public-private partnerships for a successful campaign for novel treatment options against this devastating disease.

BE-DTI': Ensemble framework for drug target interaction prediction using dimensionality reduction and active learning.

BACKGROUND AND OBJECTIVE: Drug-target interaction prediction plays an intrinsic role in the drug discovery process. Prediction of novel drugs and targets helps in identifying optimal drug therapies for various stringent diseases. Computational prediction of drug-target interactions can help to identify potential drug-target pairs and speed-up the process of drug repositioning. In our present, work we have focused on machine learning algorithms for predicting drug-target interactions from the pool of existing drug-target data. The key idea is to train the classifier using existing DTI so as to predict new or unknown DTI. However, there are various challenges such as class imbalance and high dimensional nature of data that need to be addressed before developing optimal drug-target interaction model. METHODS: In this paper, we propose a bagging based ensemble framework named BE-DTI' for drug-target interaction prediction using dimensionality reduction and active learning to deal with class-imbalanced data. Active learning helps to improve under-sampling bagging based ensembles. Dimensionality reduction is used to deal with high dimensional data. RESULTS: Results show that the proposed technique outperforms the other five competing methods in 10-fold cross-validation experiments in terms of AUC=0.927, Sensitivity=0.886, Specificity=0.864, and G-mean=0.874. CONCLUSION: Missing interactions and new interactions are predicted using the proposed framework. Some of the known interactions are removed from the original dataset and their interactions are recalculated to check the accuracy of the proposed framework. Moreover, validation of the proposed approach is performed using the external dataset. All these results show that structurally similar drugs tend to interact with similar targets.

The computation of biomarkers in pharmacokinetics with the aid of singular perturbation methods.

In pharmacokinetics, exact solutions to one-compartment models with nonlinear elimination kinetics cannot be found analytically, if dosages are assumed to be administered repetitively through extravascular routes (Tang and Xiao in J Pharmacokinet Pharmacodyn 34(6):807-827, 2007). Hence, for the corresponding impulsed dynamical system, alternative methods need to be developed to find approximate solutions. The primary purpose of this paper is to use the method of matched asymptotic expansions (Holmes Introduction to Perturbation Methods, vol 20. Springer Science & Business Media, Berlin, 2012), a singular perturbation method (Holmes, Introduction to Perturbation Methods, vol 20. Springer Science & Business Media, Berlin, 2012; Keener Principles of Applied Mathematics, Addison-Wesley, Boston, 1988), to obtain approximate solutions. With this method, we are able to rigorously determine conditions under which there is a stable periodic solution of the model equations. Furthermore, typical important biomarkers that enable the design of practical, efficient and safe drug delivery protocols, such as the time the drug concentration reaches the peak and the peak concentrations, are theoretically estimated by the perturbation method we employ.

Optimal dynamic control approach in a multi-objective therapeutic scenario: Application to drug delivery in the treatment of prostate cancer.

Numerous problems encountered in computational biology can be formulated as optimization problems. In this context, optimization of drug release characteristics or dosing schedules for anticancer agents has become a prominent area not only for the development of new drugs, but also for established drugs. However, in complex systems, optimization of drug exposure is not a trivial task and cannot be efficiently addressed through trial-error simulation exercises. Finding a solution to those problems is a challenging task which requires more advanced strategies like optimal control theory. In this work, we perform an optimal control analysis on a previously developed computational model for the testosterone effects of triptorelin in prostate cancer patients with the goal of finding optimal drug-release characteristics. We demonstrate how numerical control optimization of non-linear models can be used to find better therapeutic approaches in order to improve the final outcome of the patients.

Asymptotic analysis of a TMDD model: when a reaction contributes to the destruction of its product.

The multi-scale dynamics of a two-compartment with first order absorption Target-Mediated Drug Disposition (TMDD) pharmacokinetics model is analysed, using the Computational Singular Perturbation (CSP) algorithm. It is shown that the process evolves along two Slow Invariant Manifolds (SIMs), on which the most intense components of the model are equilibrated, so that the less intensive are the driving ones. The CSP tools allow for the identification of the components of the TMDD model that (i) constrain the evolution of the process on the SIMs, (ii) drive the system along the SIMs and (iii) generate the fast time scales. Among others, such diagnostics identify (i) the factors that determine the start and the duration of the period in which the ligand-receptor complex acts and (ii) the processes that determine its degradation rate. The counterintuitive influence of the process that transfers the ligand from the tissue to the main compartment, as it is manifested during the final stage of the process, is studied in detail.