Cenozoic evolution of the steppe-desert biome in Central Asia

The origins and development of the arid and highly seasonal steppe-desert biome in Central Asia, the largest of its kind in the world, remain largely unconstrained by existing records. It is unclear how Cenozoic climatic, geological, and biological forces, acting at diverse spatial and temporal scales, shaped Central Asian ecosystems through time. Our synthesis shows that the Central Asian steppe-desert has existed since at least Eocene times but experienced no less than two regime shifts, one at the Eocene-Oligocene Transition and one in the mid-Miocene. These shifts separated three successive "stable states," each characterized by unique floral and faunal structures. Past responses to disturbance in the Asian steppe-desert imply that modern ecosystems are unlikely to recover their present structures and diversity if forced into a new regime. This is of concern for Asian steppes today, which are being modified for human use and lost to desertification at unprecedented rates.

OP0291 TOFACITINIB FOR THE TREATMENT OF POLYARTICULAR COURSE JUVENILE IDIOPATHIC ARTHRITIS: RESULTS OF A PHASE 3, RANDOMISED, DOUBLE-BLIND, PLACEBO-CONTROLLED WITHDRAWAL STUDY

Background:

Tofacitinib is an oral JAK inhibitor that is being investigated for JIA.

Objectives:

To assess tofacitinib efficacy and safety in JIA patients (pts).

Methods:

This was a Phase 3, randomised, double-blind (DB), placebo (PBO)-controlled withdrawal study in pts aged 2−<18 years with polyarticular course JIA (pcJIA), PsA or ERA (NCT02592434). In the 18-week open-label Part 1, pts received weight-based tofacitinib doses (5 mg BID or lower). Pts with ≥JIA ACR30 response at Week (W)18 were randomised 1:1 in the DB Part 2 (W18−44) to continue tofacitinib or switch to PBO. Primary endpoint: disease flare rate by W44. Key secondary endpoints: JIA ACR50/30/70 response rates; change from Part 2 baseline (Δ) in CHAQ-DI at W44. Other efficacy endpoints: time to disease flare in Part 2; JADAS27-CRP in Parts 1 and 2. PsA/ERA pts were excluded from these efficacy analyses. Safety was evaluated in all pts up to W44.

Results:

225 enrolled pts with pcJIA (n=184), PsA (n=20) or ERA (n=21) received tofacitinib in Part 1. At W18, 173/225 (76.9%) pts entered Part 2 (pcJIA n=142, PsA n=15, ERA n=16). In pcJIA pts, disease flare rate in Part 2 was significantly lower with tofacitinib vs PBO by W44 (p=0.0031; Fig 1a). JIA ACR50/30/70 response rates (Fig 1b) and ΔCHAQ-DI (Fig 1c) at W44, and time to disease flare in Part 2 (Fig 2a), were improved with tofacitinib vs PBO. Tofacitinib reduced JADAS27-CRP in Part 1; this effect was sustained in Part 2 (Fig 2b). Overall, safety was similar with tofacitinib or PBO (Table): 77.3% and 74.1% had adverse events (AEs); 1.1% and 2.4% had serious AEs. In Part 1, 2 pts had herpes zoster (non-serious) and 3 pts had serious infections (SIs). In Part 2, SIs occurred in 1 tofacitinib pt and 1 PBO pt. No pts died.

Conclusion:

In pcJIA pts, tofacitinib vs PBO resulted in significantly fewer disease flares, and improved time to flare, disease activity and physical functioning. Tofacitinib safety was consistent with that in RA pts.

Table.

Safety in all pts

Part 1Part 2TofacitinibaN=225TofacitinibaN=88PBO N=85Pts with events, n (%)AEs153 (68.0)68 (77.3)63 (74.1)SAEs7 (3.1)1 (1.1)2 (2.4)Permanent discontinuations due to AEs26 (11.6)16 (18.2)29 (34.1)AEs of special interest Death000 Gastrointestinal perforationb000 Hepatic eventb3 (1.3)00 Herpes zoster (non-serious and serious)2 (0.9)c00 Interstitial lung diseaseb000 Major adverse cardiovascular eventsb000 Malignancy (including non-melanoma skin cancer)b000 Macrophage activation syndromeb000 Opportunistic infectionb000 SI3 (1.3)1 (1.1)d1 (1.2) Thrombotic event (deep vein thrombosis, pulmonary embolismbor arterial thromboembolism)000 Tuberculosisb000

a5 mg BID or equivalent weight-based lower dose in pts <40 kg

bAdjudicated events

cBoth non-serious

dOne SAE of pilonidal cyst repair was coded to surgical procedures instead of infections, and was inadvertently not identified as an SI. Following adjudication, the SAE did not meet opportunistic infection criteria; it is also included in the table as an SI

AE, adverse event; BID, twice daily; PBO, placebo; pts, patients; SAE, serious AE; SI, serious infection

Acknowledgments:

Study sponsored by Pfizer Inc. Medical writing support was provided by Sarah Piggott of CMC Connect and funded by Pfizer Inc.

Disclosure of Interests:

Nicolino Ruperto Grant/research support from: Bristol-Myers Squibb, Eli Lily, F Hoffmann-La Roche, GlaxoSmithKline, Janssen, Novartis, Pfizer, Sobi (paid to institution), Consultant of: Ablynx, AbbVie, AstraZeneca-Medimmune, Biogen, Boehringer Ingelheim, Bristol-Myers Squibb, Eli Lily, EMD Serono, GlaxoSmithKline, Hoffmann-La Roche, Janssen, Merck, Novartis, Pfizer, R-Pharma, Sanofi, Servier, Sinergie, Sobi, Takeda, Speakers bureau: Ablynx, AbbVie, AstraZeneca-Medimmune, Biogen, Boehringer Ingelheim, Bristol-Myers Squibb, Eli Lily, EMD Serono, GlaxoSmithKline, Hoffmann-La Roche, Janssen, Merck, Novartis, Pfizer, R-Pharma, Sanofi, Servier, Sinergie, Sobi, Takeda, Olga Synoverska Speakers bureau: Sanofi, Tracy Ting: None declared, Carlos Abud-Mendoza Speakers bureau: Eli Lilly, Pfizer Inc, Alberto Spindler Speakers bureau: Eli Lilly, Yulia Vyzhga Grant/research support from: Pfizer Inc, Katherine Marzan Grant/research support from: Novartis, Vladimir Keltsev: None declared, Irit Tirosh: None declared, Lisa Imundo: None declared, Rita Jerath: None declared, Daniel Kingsbury: None declared, Betül Sözeri: None declared, Sheetal Vora: None declared, Sampath Prahalad Grant/research support from: Novartis, Elena Zholobova Grant/research support from: Novartis and Pfizer Inc, Speakers bureau: AbbVie, Novartis, Pfizer Inc and Roche, Yonatan Butbul Aviel: None declared, Vyacheslav Chasnyk: None declared, Melissa Lerman Grant/research support from: Amgen, Kabita Nanda Grant/research support from: Abbott, AbbVie, Amgen and Roche, Heinrike Schmeling Grant/research support from: Janssen, Pfizer Inc, Roche and USB Bioscience, Heather Tory: None declared, Yosef Uziel Speakers bureau: Pfizer Inc, Diego O Viola Grant/research support from: Bristol-Myers Squibb, GSK, Janssen and Pfizer Inc, Speakers bureau: AbbVie and Bristol-Myers Squibb, Holly Posner Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Keith Kanik Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Ann Wouters Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Cheng Chang Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Richard Zhang Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Irina Lazariciu Consultant of: Pfizer Inc, Employee of: IQVIA, Ming-Ann Hsu Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Ricardo Suehiro Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Alberto Martini Consultant of: AbbVie, Eli Lily, EMD Serono, Janssen, Novartis, Pfizer, UCB, Daniel J Lovell Consultant of: Abbott (consulting and PI), AbbVie (PI), Amgen (consultant and DSMC Chairperson), AstraZeneca, Boehringer Ingelheim, Bristol-Myers Squibb (PI), Celgene, Forest Research (DSMB Chairman), GlaxoSmithKline, Hoffman-La Roche, Janssen (co-PI), Novartis (consultant and PI), Pfizer (consultant and PI), Roche (PI), Takeda, UBC (consultant and PI), Wyeth, Employee of: Cincinnati Children’s Hospital Medical Center, Speakers bureau: Wyeth, Hermine Brunner Consultant of: Hoffman-La Roche, Novartis, Pfizer, Sanofi Aventis, Merck Serono, AbbVie, Amgen, Alter, AstraZeneca, Baxalta Biosimilars, Biogen Idec, Boehringer, Bristol-Myers Squibb, Celgene, EMD Serono, Janssen, MedImmune, Novartis, Pfizer, and UCB Biosciences, Speakers bureau: GSK, Roche, and Novartis

FRI0465 TOFACITINIB POPULATION PHARMACOKINETICS IN CHILDREN WITH JUVENILE IDIOPATHIC ARTHRITIS: A POOLED ANALYSIS OF DATA FROM THREE CLINICAL STUDIES

Background:

Tofacitinib is an oral JAK inhibitor that is being investigated for juvenile idiopathic arthritis (JIA).

Objectives:

To describe tofacitinib pharmacokinetics (PK) in patients with JIA, identify potential covariates accounting for variability in exposure, assess the formulation effect of oral solution vs tablet and propose a simplified dosing regimen.

Methods:

This was a pooled analysis of data from 3 tofacitinib clinical studies in patients with JIA aged 2−<18 years: a Phase 1, open-label (OL), non-randomised study (NCT01513902); a Phase 3, randomised, double-blind, placebo-controlled, withdrawal study (NCT02592434); and an OL long-term extension study (NCT01500551). Tofacitinib was dosed at 5 mg twice daily (BID) in patients ≥40 kg or at body weight (BW)-based lower doses BID in patients <40 kg, to achieve average concentrations (Cavg) comparable with those in patients receiving 5 mg BID. A sparse PK sampling scheme was applied, and the plasma samples were assayed using a validated, sensitive and specific high-performance liquid chromatography tandem mass spectrometric method (lower limit of quantification = 0.100 ng/mL). A nonlinear mixed-effects modelling approach was used for the population PK model, and population parameter variability was assumed to be log-normally distributed. Covariates relating to patient demographics, disease characteristics, concomitant medications and formulation (oral solution vs tablet) were selected using a stepwise covariate modelling approach, and parameter-covariate relationships were evaluated using stepwise forward-inclusion (p<0.05) backward-deletion (p<0.001) procedures. The effect of time-varying BW on oral clearance (CL/F) and apparent volume of distribution (V/F) was characterised using an allometric model. Final model quality was assessed by Visual Predictive Checks (VPCs).

Results:

Of 246 patients in the analysis, 74.0% were female; 87.8% were white, 2% were black, 10.2% were ‘other’ races and no patients were Asian. Median (range) BW was 46.3 (11.1−121.8) kg. Initially, 100 patients received oral solution and 146 patients received tablets; 11 patients switched formulations during the studies. A one compartment disposition model with first-order absorption and a lag time sufficiently described the data. Final estimates for CL/F, V/F and the first-order absorption rate constant (ka) for tablets were 26.1 L/hr, 89.2 L and 2.78 hr-1, respectively. The only statistically significant covariate was a formulation effect on ka. All parameters were estimated adequately. Estimated allometric exponents were 0.310 for CL/F and 0.537 for V/F. Absorption was described with an estimated lag time of 0.186 hr, and the oral solution had a 1.64-fold faster absorption rate vs the tablet. VPCs sufficiently described the observed data over time, across BWs and ages. Given the PK characterisation and variability in patients with JIA, a simplified dosing scheme was proposed, targeting Cavgvalues equivalent to those in patients receiving 5 mg BID: 3.2 mg BID solution in patients 10−<20 kg; 4 mg BID solution in patients 20−<40 kg; and 5 mg BID tablet or solution in patients ≥40 kg.

Conclusion:

Tofacitinib population PK in patients with JIA were adequately described by a one compartment model parameterised in terms of CL/F, V/F and first-order absorption with a lag time. Drug absorption from the oral solution was faster than from the tablet. Tofacitinib does not require dose modification or restrictions for any covariates, except BW, to account for differences in Cavg. Based on the results of this analysis, a simplified BW-based dosing regimen was proposed.

Acknowledgments:

Study sponsored by Pfizer Inc. Medical writing support was provided by Sarah Piggott of CMC Connect and funded by Pfizer Inc.

Disclosure of Interests:

Camille Vong Shareholder of: Pfizer Inc, at time of analysis, Employee of: Pfizer Inc, at time of analysis, Xiaoxing Wang Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Anasuya Hazra Shareholder of: Pfizer Inc, at time of analysis, Employee of: Pfizer Inc, at time of analysis, Arnab Mukherjee Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Timothy Nicholas Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Cheng Chang Shareholder of: Pfizer Inc, Employee of: Pfizer Inc

0659 A Safe and Precise Tongue Base Surgery for Obstructive Sleep Apnea: Real-Time Intraoperative Ultrasound-Assisted Transoral Robotic Surgery

Introduction

Lingual artery (LA) injury is a devastating complication of tongue base surgery. Compared with the anatomic findings of computed tomography angiography (CTA), intraoperative blade of mouth gag might change the thickness of base of tongue (BOT) and anatomy of LA. We aimed to investigate the position of LA in the BOT with intraoperative ultrasound (IOU) imaging during transoral robotic surgery (TORS), and evaluate the bleeding complications when assisted with / without IOU.

Methods

Adult obstructive sleep apnea (OSA) patients who received TORS in BOT resection were recruited since 2016. Assessment tools were pre-op over-night hospital polysomnography (PSG) and anatomy-based Friedman Staging System. Ultrasound imaging was utilized to identify anatomic parameters of LA in BOT, including distance to midline, arterial depth and diameter.

Results

Ninety-three OSA patients (82 male, 88.2%) were analyzed. The mean age was 42.2±10.0 years old and body mass index was 29.2±4.5 kg/m2. The average apnea hypopnea index (AHI) was 58.1±21.4 events/hour. There were 66 (71.0%), 24 (25.8%) and 3 (3.2%) patients in Friedman stages II, III and IV, respectively. Seventy patients underwent TORS with IOU had shorter operation time (191.7±3.8 minutes) than 23 patients without IOU (220.1±6.6 minutes), less total blood loss (11.3±10.8 versus 19.6±26.7 ml), and more BOT tissue reduction volume (7.1±2.5 versus 3.9±1.6 ml). Significant predictors of arterial depth were higher AHI level during rapid-eye-movement (REM) sleep stage (p=0.038), bigger tonsil size (p=0.034) and more elevated Friedman tongue position (p=0.012). Postoperative complication associated with LA injury was not found in the patients with use of IOU.

Conclusion

When tongue retracted with blade, the distance to midline and depth of LA were altered in BOT. With IOU assisted, surgeon could identify LA position confidently. It is expectable to maximize efficiency and minimize catastrophic bleeding complications when OSA patients received TORS in BOT resection.

Support

nil

Bioaccumulation of Hg in Rice Leaf Facilitates Selenium Bioaccumulation in Rice (Oryza sativa L.) Leaf in the Wanshan Mercury Mine

Mercury (Hg) bioaccumulation in rice poses a health issue for rice consumers. In rice paddies, selenium (Se) can decrease the bioavailability of Hg through forming the less bioavailable Hg selenides (HgSe) in soil. Rice leaves can directly uptake a substantial amount of elemental Hg from the atmosphere, however, whether the bioaccumulation of Hg in rice leaves can affect the bioaccumulation of Se in rice plants is not known. Here, we conducted field and controlled studies to investigate the bioaccumulation of Hg and Se in the rice-soil system. In the field study, we observed a significantly positive correlation between Hg concentrations and BAFs of Se in rice leaves (r² = 0.60, p < 0.01) collected from the Wanshan Mercury Mine, SW China, suggesting that the bioaccumulation of atmospheric Hg in rice leaves can facilitate the uptake of soil Se, perhaps through the formation of Hg-Se complex in rice leaves. This conclusion was supported by the controlled study, which observed significantly higher concentrations and BAFs of Se in rice leaf at a high atmospheric Hg site at WMM, compared to a low atmospheric Hg site in Guiyang, SW China.

[The incidence of school violence and its relevant factors among senior students of rural primary schools in Guizhou and Anhui Province in 2016]

Objective: To understand the incidence of school violence and its related factors among senior students in rural primary schools in Guizhou and Anhui provinces in 2016. Methods: From September to November 2016, one county was randomly selected from 47 impoverished counties of Guizhou and one county was randomly selected from 18 impoverished counties of Anhui Province by using multi-stage cluster sampling method. A total of 4 schools were randomly selected from primary schools which had more than 70% students with rural resident registration in each selected county, and 3 classes were randomly selected from fifth and sixth grades of each selected school. All students in each selected class were included in this study. A total of 2 521 students were included, and 2 506 valid questionnaires were collected with an effective response rate about 99.4%. Self-designed questionnaires were used to collect data on individual information, family information, and the incidence of school violence with related factors. Multivariable logistic regression was conducted to analyze related factors of school violence. Results: The incidence of violence in rural primary schools was 50.4% (1 264/2 506) in 2016. The number of students who had physical assault, fought or been bullied was 682 (27.2%), 689 (27.5%) and 764 (30.5%), respectively. The results of multivariable logistic regression showed that the OR (95%CI) values of incidence of school violence for male students was 2.13(1.78-2.54) compared with female students; and the OR(95%CI) values of incidence of school violence for students with high peer acceptance, high family intimacy, more physical activity and mother who had education background of junior high school, senior high school, technical secondary school and junior college, bachelor's degree or above were 0.39 (0.26-0.57), 0.47 (0.40-0.57), 0.64 (0.52-0.78), 0.79 (0.66-0.96), and 0.65 (0.46-0.92) compared with those with low peer acceptance, low family intimacy, less physical activity and mother who had primary education and below. Conclusion: The incidence of violence in rural primary schools is not optimistic. Gender, peer acceptance, family intimacy, physical activity and mother education are related to school violence.