Prediction of disease progression, treatment response and dropout in chronic obstructive pulmonary disease (COPD).

Drug development in chronic obstructive pulmonary disease (COPD) has been characterised by unacceptably high failure rates. In addition to the poor sensitivity in forced expiratory volume in one second (FEV1), numerous causes are known to contribute to this phenomenon, which can be clustered into drug-, disease- and design-related factors. Here we present a model-based approach to describe disease progression, treatment response and dropout in clinical trials with COPD patients

Prediction of lung exposure to anti-tubercular drugs using plasma pharmacokinetic data: implications for dose selection

The development of novel candidate molecules for tuberculosis remains challenging, as drug distribution into the target tissue is not fully characterised in preclinical models of infection. Often antitubercular human dose selection is derived from pharmacokinetic data in plasma. Here, we explore whether whole-body physiologically-based pharmacokinetic (PBPK) modelling enables the prediction of lung exposure to anti-tubercular drugs in humans. Whole-body PBPK models were developed for rifampicin, isoniazid, pyrazinamide, and ethambutol using plasma data in mice as basis for the prediction of lung exposure. Model parameters were subsequently used to extrapolate disposition properties from mouse and determine lung:plasma ratio in humans. Model predictions were compared to biopsy data from patients. Predictions were deemed adequate if they fell within two-fold range of the observations. The concentration vs time profiles in lung were adequately predicted in mice. Isoniazid and pyrazinamide lung exposures were predicted to be comparable to plasma levels, whereas ethambutol lung exposure was predicted to be higher than in plasma. Lung:plasma ratio in humans could be reasonably predicted from preclinical data, but was highly dependent on the distribution model. This analysis showed that plasma pharmacokinetics may be used in conjunction with PBPK modelling to derive lung tissue exposure in mice and humans during early lead optimisation phase. However, the impact of uncertainty in predicted tissue exposure due to distribution should be always investigated through a sensitivity analysis when only plasma data is available. Despite these limitations, insight into lung tissue distribution represents a critical step for the dose rationale in tuberculosis patients

Application of machine learning in combination with mechanistic modeling to predict plasma exposure of small molecules

Prediction of a new molecule’s exposure in plasma is a critical first step toward understanding its efficacy/toxicity profile and concluding whether it is a possible first-in-class, best-in-class candidate. For this prediction, traditional pharmacometrics use a variety of scaling methods that are heavily based on pre-clinical pharmacokinetic (PK) data. We here propose a novel framework based on which preclinical exposure prediction is performed by applying machine learning (ML) in tandem with mechanism-based modeling. In our proposed method, a relationship is initially established between molecular structure and physicochemical (PC)/PK properties using ML, and then the ML-driven PC/PK parameters are used as input to mechanistic models that ultimately predict the plasma exposure of new candidates. To understand the feasibility of our proposed framework, we evaluated a number of mechanistic models (1-compartment, physiologically based pharmacokinetic (PBPK)), PBPK distribution models (Berezhkovskiy, PK-Sim standard, Poulin and Theil, Rodgers and Rowland, and Schmidt), and PBPK parameterizations (using in vivo, or in vitro clearance). For most of the scenarios tested, our results demonstrate that PK profiles can be adequately predicted based on the proposed framework. Our analysis further indicates some limitations when liver microsomal intrinsic clearance (CLint) is used as the only clearance pathway and underscores the necessity of investigating the variability emanating from the different distribution models when providing PK predictions. The suggested approach aims at earlier exposure prediction in the drug development process so that critical decisions on molecule screening, chemistry design, or dose selection can be made as early as possible

Predictors of seizure outcomes in children with tuberous sclerosis complex and intractable epilepsy undergoing resective epilepsy surgery: an individual participant data meta-analysis.

ObjectiveTo perform a systematic review and individual participant data meta-analysis to identify preoperative factors associated with a good seizure outcome in children with Tuberous Sclerosis Complex undergoing resective epilepsy surgery.Data sourcesElectronic databases (MEDLINE, EMBASE, CINAHL and Web of Science), archives of major epilepsy and neurosurgery meetings, and bibliographies of relevant articles, with no language or date restrictions.Study selectionWe included case-control or cohort studies of consecutive participants undergoing resective epilepsy surgery that reported seizure outcomes. We performed title and abstract and full text screening independently and in duplicate. We resolved disagreements through discussion.Data extractionOne author performed data extraction which was verified by a second author using predefined data fields including study quality assessment using a risk of bias instrument we developed. We recorded all preoperative factors that may plausibly predict seizure outcomes.Data synthesisTo identify predictors of a good seizure outcome (i.e. Engel Class I or II) we used logistic regression adjusting for length of follow-up for each preoperative variable.ResultsOf 9863 citations, 20 articles reporting on 181 participants were eligible. Good seizure outcomes were observed in 126 (69%) participants (Engel Class I: 102(56%); Engel class II: 24(13%)). In univariable analyses, absence of generalized seizure semiology (OR = 3.1, 95%CI = 1.2-8.2, p = 0.022), no or mild developmental delay (OR = 7.3, 95%CI = 2.1-24.7, p = 0.001), unifocal ictal scalp electroencephalographic (EEG) abnormality (OR = 3.2, 95%CI = 1.4-7.6, p = 0.008) and EEG/Magnetic resonance imaging concordance (OR = 4.9, 95%CI = 1.8-13.5, p = 0.002) were associated with a good postoperative seizure outcome.ConclusionsSmall retrospective cohort studies are inherently prone to bias, some of which are overcome using individual participant data. The best available evidence suggests four preoperative factors predictive of good seizure outcomes following resective epilepsy surgery. Large long-term prospective multicenter observational studies are required to further evaluate the risk factors identified in this review

Lattice Resistance and Peierls Stress in Finite-size Atomistic Dislocation Simulations

Atomistic computations of the Peierls stress in fcc metals are relatively scarce. By way of contrast, there are many more atomistic computations for bcc metals, as well as mixed discrete-continuum computations of the Peierls-Nabarro type for fcc metals. One of the reasons for this is the low Peierls stresses in fcc metals. Because atomistic computations of the Peierls stress take place in finite simulation cells, image forces caused by boundaries must either be relaxed or corrected for if system size independent results are to be obtained. One of the approaches that has been developed for treating such boundary forces is by computing them directly and subsequently subtracting their effects, as developed by V. B. Shenoy and R. Phillips [Phil. Mag. A, 76 (1997) 367]. That work was primarily analytic, and limited to screw dislocations and special symmetric geometries. We extend that work to edge and mixed dislocations, and to arbitrary two-dimensional geometries, through a numerical finite element computation. We also describe a method for estimating the boundary forces directly on the basis of atomistic calculations. We apply these methods to the numerical measurement of the Peierls stress and lattice resistance curves for a model aluminum (fcc) system using an embedded-atom potential.Comment: LaTeX 47 pages including 20 figure

Physiologically-based pharmacokinetic modeling of quinidine to establish a CYP3A4, P-gp, and CYP2D6 drug-drug-gene interaction network

The antiarrhythmic agent quinidine is a potent inhibitor of cytochrome P450 (CYP) 2D6 and P-glycoprotein (P-gp) and is therefore recommended for use in clinical drug-drug interaction (DDI) studies. However, as quinidine is also a substrate of CYP3A4 and P-gp, it is susceptible to DDIs involving these proteins. Physiologically-based pharmacokinetic (PBPK) modeling can help to mechanistically assess the absorption, distribution, metabolism, and excretion processes of a drug and has proven its usefulness in predicting even complex interaction scenarios. The objectives of the presented work were to develop a PBPK model of quinidine and to integrate the model into a comprehensive drug-drug(-gene) interaction (DD(G)I) network with a diverse set of CYP3A4 and P-gp perpetrators as well as CYP2D6 and P-gp victims. The quinidine parent-metabolite model including 3-hydroxyquinidine was developed using pharmacokinetic profiles from clinical studies after intravenous and oral administration covering a broad dosing range (0.1-600 mg). The model covers efflux transport via P-gp and metabolic transformation to either 3-hydroxyquinidine or unspecified metabolites via CYP3A4. The 3-hydroxyquinidine model includes further metabolism by CYP3A4 as well as an unspecific hepatic clearance. Model performance was assessed graphically and quantitatively with greater than 90% of predicted pharmacokinetic parameters within two-fold of corresponding observed values. The model was successfully used to simulate various DD(G)I scenarios with greater than 90% of predicted DD(G)I pharmacokinetic parameter ratios within two-fold prediction success limits. The presented network will be provided to the research community and can be extended to include further perpetrators, victims, and targets, to support investigations of DD(G)Is.Horizon 2020 (H2020)Personalised Therapeutic

Mapping the Anthocyaninless (anl) Locus in Rapid-Cycling Brassica rapa (RBr) to Linkage Group R9

<p>Abstract</p> <p>Background</p> <p>Anthocyanins are flavonoid pigments that are responsible for purple coloration in the stems and leaves of a variety of plant species. <it>Anthocyaninless </it>(<it>anl</it>) mutants of <it>Brassica rapa </it>fail to produce anthocyanin pigments. In rapid-cycling <it>Brassica rapa</it>, also known as Wisconsin Fast Plants, the anthocyaninless trait, also called non-purple stem, is widely used as a model recessive trait for teaching genetics. Although anthocyanin genes have been mapped in other plants such as <it>Arabidopsis thaliana</it>, the <it>anl </it>locus has not been mapped in any <it>Brassica </it>species.</p> <p>Results</p> <p>We tested primer pairs known to amplify microsatellites in <it>Brassicas </it>and identified 37 that amplified a product in rapid-cycling <it>Brassica rapa</it>. We then developed three-generation pedigrees to assess linkage between the microsatellite markers and <it>anl</it>. 22 of the markers that we tested were polymorphic in our crosses. Based on 177 F₂offspring, we identified three markers linked to <it>anl </it>with LOD scores ≥ 5.0, forming a linkage group spanning 46.9 cM. Because one of these markers has been assigned to a known <it>B. rapa </it>linkage group, we can now assign the <it>anl </it>locus to <it>B. rapa </it>linkage group R9.</p> <p>Conclusion</p> <p>This study is the first to identify the chromosomal location of an anthocyanin pigment gene among the <it>Brassicas</it>. It also connects a classical mutant frequently used in genetics education with molecular markers and a known chromosomal location.</p

mTORC1 Inhibition via Rapamycin Promotes Triacylglycerol Lipolysis and Release of Free Fatty Acids in 3T3â L1 Adipocytes

Signaling by mTOR complex 1 (mTORC1) promotes anabolic cellular processes in response to growth factors, nutrients, and hormonal cues. Numerous clinical trials employing the mTORC1 inhibitor rapamycin (aka sirolimus) to immunoâ suppress patients following organ transplantation have documented the development of hypertriglyceridemia and elevated serum free fatty acids (FFA). We therefore investigated the cellular role of mTORC1 in control of triacylglycerol (TAG) metabolism using cultured murine 3T3â L1 adipocytes. We found that treatment of adipocytes with rapamycin reduced insulinâ stimulated TAG storage ~50%. To determine whether rapamycin reduces TAG storage by upregulating lipolytic rate, we treated adipocytes in the absence and presence of rapamycin and isoproterenol, a β2â adrenergic agonist that activates the cAMP/protein kinase A (PKA) pathway to promote lipolysis. We found that rapamycin augmented isoproterenolâ induced lipolysis without altering cAMP levels. Rapamycin enhanced the isoproterenolâ stimulated phosphorylation of hormone sensitive lipase (HSL) on Serâ 563 (a PKA site), but had no effect on the phosphorylation of HSL S565 (an AMPK site). Additionally, rapamycin did not affect the isoproterenolâ mediated phosphorylation of perilipin, a protein that coats the lipid droplet to initiate lipolysis upon phosphorylation by PKA. These data demonstrate that inhibition of mTORC1 signaling synergizes with the βâ adrenergicâ cAMP/PKA pathway to augment phosphorylation of HSL to promote hormoneâ induced lipolysis. Moreover, they reveal a novel metabolic function for mTORC1; mTORC1 signaling suppresses lipolysis, thus augmenting TAG storage.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141428/1/lipd1089.pd

Consensus protocol for EEG and amplitude-integrated EEG assessment and monitoring in neonates

The aim of this work is to establish inclusive guidelines on electroencephalography (EEG) applicable to all neonatal intensive care units (NICUs). Guidelines on ideal EEG monitoring for neonates are available, but there are significant barriers to their implementation in many centres around the world. These include barriers due to limited resources regarding the availability of equipment and technical and interpretive round-the-clock personnel. On the other hand, despite its limitations, amplitude-integrated EEG (aEEG) (previously called Cerebral Function Monitor [CFM]) is a common alternative used in NICUs. The Italian Neonatal Seizure Collaborative Network (INNESCO), working with all national scientific societies interested in the field of neonatal clinical neurophysiology, performed a systematic literature review and promoted interdisciplinary discussions among experts (neonatologists, paediatric neurologists, neurophysiologists, technicians) between 2017 and 2020 with the aim of elaborating shared recommendations. A consensus statement on videoEEG (vEEG) and aEEG for the principal neonatal indications was established. The authors propose a flexible frame of recommendations based on the complementary use of vEEG and aEEG applicable to the various neonatal units with different levels of complexity according to local resources and specific patient features. Suggestions for promoting cooperation between neonatologists, paediatric neurologists, and neurophysiologists, organisational restructuring, and teleneurophysiology implementation are provided

Prediction of Disease Progression, Treatment Response and Dropout in Chronic Obstructive Pulmonary Disease (COPD)

Drug development in chronic obstructive pulmonary disease (COPD) has been characterised by unacceptably high failure rates. In addition to the poor sensitivity in forced expiratory volume in one second (FEV1), numerous causes are known to contribute to this phenomenon, which can be clustered into drug-, disease- and design-related factors. Here we present a model-based approach to describe disease progression, treatment response and dropout in clinical trials with COPD patients