Actelion (Switzerland)

Respiratory viruses circulate yearly with strain-specific patterns. Although SARS-CoV-2 and Influenza A/B genomic surveillance is well-developed, most respiratory viruses are unevenly monitored, lacking geographical diversity to capture wider population dynamics. Consequently, insights into respiratory virus evolution are limited. We investigated the genetic diversity of these viruses within one country. During the 2023/24 season, we conducted whole-genome sequencing of 1'129 clinical samples using a hybrid-capture protocol. These samples were pre-tested by real-time PCR panels throughout Switzerland. Leveraging publicly-available full-length genomes, we constructed background datasets representative of geographical diversity and built 16 phylogenies covering the diversity of high-quality viral genomes produced from this study. We detected viral genomes in 632 samples, including 55.6% multi-positive infections (n = 352/632), and recovered high-quality genomes in 73% (n = 461/632) of cases, including 3.9% of co-infections (n = 18/461), from 454 PCR-positive and 7 PCR-negative samples. For 56% of samples (n = 634/1129), the hybrid-capture detection result was concordant with the PCR-result (at least one strain detected by PCR was also detected by sequencing, in addition to other viruses). The four most prevalent viruses were Influenza A/H1N1, SARS-CoV-2, RSV-A, and HPIV-3, and their seasonal spread was consistent with national wastewater monitoring and influenza-like illness reports. Swiss viral genomes were representative of the global genomic diversity, with evidence for multiple introductions into Switzerland, and we identified putative Swiss clusters. In this proof-of-concept study, we focus on 3 viruses (Influenza A/H1N1, RSV-A/B, and HPIV-3), and we demonstrate the streamlined implementation of a broad respiratory virus genomic surveillance workflow with an off-the-shelf protocol and publicly-available software. In addition, we highlight additional evolutionary insights that can only be derived from genomic surveillance. Going forward, this dataset will be a useful resource for future investigations into respiratory viral genomic diversity.

Selexipag is an oral selective prostacyclin receptor agonist approved for the treatment of pulmonary arterial hypertension (PAH) in adults. To date, no treatment targeting the prostacyclin pathway is approved for pediatric patients. Our goal is to identify a pediatric dose regimen that results in comparable exposures to selexipag and its active metabolite JNJ‐68006861 as those shown to be efficacious in adult PAH patients. Extrapolation from the population pharmacokinetic (PK) model developed in adults (GRIPHON study; NCT01106014) resulted in the definition of three different pediatric body weight groups (≥9 to <25 kg, ≥25 to <50 kg, and ≥50 kg) with corresponding starting doses (100, 150, and 200 μg twice daily) and maximum allowed doses (800, 1200, and 1600 μg twice daily). The proposed pediatric dose regimen was subsequently tested in a clinical study (NCT03492177), including 63 pediatric PAH patients ≥2 to <18 years of age and a body weight range of 9.9–93.5 kg. The body weight‐adjusted dose regimen for selexipag resulted in comparable systemic exposures to selexipag and its active metabolite in pediatric patients as previously observed in adult PAH patients. Updating the adult selexipag population PK model provided overall consistent parameters and confirmed that the PK characteristics of selexipag and its active metabolite were comparable between pediatric and adult patients. The presented selexipag dose regimen for pediatric PAH patients is considered appropriate for continuing the clinical evaluation of the safety and efficacy of selexipag in pediatric patients ≥2 years of age.

Portopulmonary hypertension (PoPH) occurs in patients with advanced liver disease and can be a contraindication to liver transplant (LT). Improvement of hemodynamic parameters with pulmonary arterial hypertension (PAH) therapies (including endothelin receptor antagonists [ERAs]) may help some patients to become eligible for LT. We conducted a retrospective secondary data analysis to describe the clinical course and management of PoPH in patients on a US registry LT waitlist and outcomes in patients receiving an ERA. At the time of LT waitlist entry (1996-2019), patient characteristics and disease severity were similar in the 685 patients with PoPH enrolled overall (LT waitlist data set) and the 420 of them who underwent LT (LT data set). Most patients (92.0%) had a model for end-stage liver disease exception granted before entering the LT waitlist. Patients spent a median of 8.9 mo (interquartile range, 3.7-19.7) on the LT waitlist before undergoing LT. Overall, 77.1% of patients received PAH treatment at LT waitlist entry (ERAs, 30.1%). Hemodynamic parameters improved in ≥95% of patients between the first assessment versus the second (median interval, 9 mo) and last assessments (median interval, 14 mo). At the first assessment, 49.6% of patients had mean pulmonary arterial pressure ≥45 mm Hg versus 2.6% and 1.8% of patients at the second and last assessments, respectively; 47.5% of patients had pulmonary vascular resistance >450 dynes·s/cm5 versus 0.9% and 0.2% of patients at the second and last assessments. One-year survival was 90.6% (95% confidence interval [CI], 87.6-92.9) following LT waitlist entry and was 86.4% (95% CI, 82.6-89.5) after LT; 5-y survival was 67.4% (95% CI, 60.0-73.8) while on the LT waitlist (before LT) and was 75.6% (95% CI, 70.4-80.0) following LT. This large US study of patients with PoPH on an LT waitlist confirms that effective PAH treatments can help patients achieve acceptable hemodynamics, providing the opportunity to undergo LT.

Pulmonary arterial hypertension (PAH) is a rare subgroup of pulmonary hypertension (PH). Claims and administrative databases can be particularly important for research in rare diseases; however, there is a lack of validated algorithms to identify PAH patients using administrative codes. We aimed to measure the accuracy of code-based PAH algorithms against the true clinical diagnosis by right heart catheterization (RHC). This study evaluated algorithms in patients who were recorded in two linkable data assets: the Stanford Healthcare administrative electronic health record database and the Stanford Vera Moulton Wall Center clinical PH database (which records each patient's RHC diagnosis). We assessed the sensitivity and specificity achieved by 16 algorithms (six published). In total, 720 PH patients with linked data available were included and 558 (78%) of these were PAH patients. Algorithms consisting solely of a P(A)H-specific diagnostic code classed all or almost all PH patients as PAH (sensitivity >97%, specificity <12%) while multicomponent algorithms with well-defined temporal sequences of procedure, diagnosis and treatment codes achieved a better balance of sensitivity and specificity. Specificity increased and sensitivity decreased with increasing algorithm complexity. The best-performing algorithms, in terms of fewest misclassified patients, included multiple components (e.g., PH diagnosis, PAH treatment, continuous enrollment for ≥6 months before and ≥12 months following index date) and achieved sensitivities and specificities of around 95% and 38%, respectively. Our findings help researchers tailor their choice and design of code-based PAH algorithms to their research question and demonstrate the importance of including well-defined temporal components in the algorithms.

IntroductionCases of severe disease reactivation have been reported after natalizumab and fingolimod discontinuation.AimThe objective of this post-hoc analysis was to evaluate disease activity after short-term ponesimod-in- terruption versus after teriflunomide-washout.MethodsIn OPTIMUM, relapsing MS(RMS) patients were randomised(1:1) to ponesimod20mg or terifluno- mide14mg once-daily for up to 108weeks.Patients who completed the 108-week double-blind treat- ment-period and underwent accelerated elimination procedure for teriflunomide were eligible to enter the OLE(open-label extension study) and receive ponesimod20mg once-daily.ResultsOf 1133 patients in OPTIMUM, 877(77.4%)patients (439PP and 438TP)were enrolled in OLE.Duration of treatment interruption ranged from 13 to 51days(median 16 days).In time to first confirmed relapse at 3months, PPgroup(ponesimod OPTIMUM/ponesimod OLE) had median-time of 49.5days while TPgroup(- teriflunomide OPTIMUM/ponesimod OLE) had 41.0days.At 6months, PPgroup had median-time of 70.0days while TPgroup-44.0days. By OLEMonth3, confirmed relapses in 27PP versus 46TP patients(Kaplan- Meier[K-M] estimates 6.5%and10.8%log-rank p=0.018).By OLE Month6, confirmed relapses in 44PP versus 55TP patients(K-M estimates 11.6%and13.5%log-rank p=0.207). In CUAL analysis of MRI at OLE Week48 compared to end of treatment OPTIMUM, mean-number of CUALs was1.638 in the PPgroup(n=210) and1.693 in the TPgroup(n=231)(p=NS).ConclusionThese findings suggest that disease activity after interruption of ponesimod does not exceed the disease activity observed after teriflunomide washout-a DMT not associated with ‘rebound’ after treatment interruption.

Topic: 14. Myeloma and other monoclonal gammopathies - Clinical Background: LocoMMotion (NCT04035226) is the first prospective observational study to assess effectiveness and safety of real-life standard of care (SOC) treatments (tx) for triple-class exposed (TCE) patients (pts) with relapsed/refractory multiple myeloma (RRMM). Effectiveness assessment by response review committee (RRC) (N=248; median follow-up 16.1 months [mo]) showed a low overall response rate (ORR) to SOC tx (31.5% [95%CI 25.7–37.6]), rapid disease progression (median progression-free survival [mPFS], 4.6 mo [95%CI 3.9–5.6]), and short median overall survival (mOS, 13.8 mo [95%CI 10.8–18.5]) (Moreau et al, IMS 2022, data cut-off Nov 2021). Here we report final results with longer median study follow-up and details of subsequent lines of therapy (LOT). Aims: To report effectiveness and safety data from final analysis of LocoMMotion study. Methods: TCE pts (≥18 yr) with documented MM, either double refractory to a PI and IMiD or with ≥3 prior LOT were enrolled after giving informed consent. Pts had documented progressive disease (PD) since last LOT and Eastern Cooperative Oncology Group performance status ≤1. Pt-level data and choice of tx (index LOT [ie, first tx upon study enrollment; approved therapies only] and subsequent LOT [experimental tx allowed]) were collected for 24 mo after first index LOT dosing of last pt enrolled. Responses were assessed by RRC per International Myeloma Working Group response criteria. Primary effectiveness endpoint was ORR, secondary endpoints included additional effectiveness endpoints and safety. Variables were summarized using descriptive statistics. ORR was reported with corresponding 95% Clopper-Pearson (exact) CIs. Time-to-event data were summarized by Kaplan-Meier methods. Results: Study period was Aug 2019 to Oct 2022 with median follow-up 26.4 mo (95%CI 25.0–28.1). N=248, median age 68 yr (range 41–89), and 135 pts (54.4%) were male. At baseline, pts had received a median of 4 prior LOT (range 2–13), 182 (73.4%) were triple-class refractory, 229 (92.3%) were refractory to last prior LOT, and 160 (64.5%) pts had prior stem cell transplantation (SCT). 91 unique tx regimens were used in index LOT, 162 (65.3%) pts had ≥3 drugs, 6 (2.4%) had SCT. Median duration of treatment (mDOT) was 4.0 mo (range 0.1–33.6). ORR was 31.9% (95%CI 26.1–38.0); median duration of response was 7.4 mo (95%CI 4.9–11.1), mPFS and mOS were 4.6 mo (95%CI 3.9–5.6) and 13.8 mo (95%CI 10.8–17.0), respectively. 12- and 24-mo PFS rates were 21.0% (95%CI 15.3–27.3) and 10.5% (95%CI 6.1–16.3). 12- and 24-mo OS rates were 53.4% (95%CI 46.7–59.6) and 33.7% (95%CI 27.3–40.2). 152 pts (61.3%) had subsequent LOT (1 subsequent LOT only n=78; ≥2 subsequent LOT n=74; Table 1). mPFS2 (ie, from start of study through subsequent LOT1) was 10.8 mo (95%CI 8.4–13.0). 79 different regimens were used in subsequent LOT1 with mDOT 2.8 mo (range 0–29.7). BCMA-targeted therapy use increased to 24.2% in subsequent LOT from 2.8% in index LOT. During index LOT 86.7% of pts experienced an adverse event (AE), most commonly cytopenias; 13 (5.2%) pts developed a second primary malignancy (index LOT n=5; after index LOT n=8). 158 pts (63.7%) died during the study; 67.7% of deaths were due to PD and 15.8% due to AEs (Table 2). Summary/Conclusion: Final analysis (median study follow-up >2 yr) of the first prospective study of real-life tx for TCE pts with RRMM shows a lack of well-established SOC, rapid disease progression, and poor survival. Despite the wide range of tx used during the study, including subsequent LOT, these results highlight the urgent need for new therapeutic options for this populationKeywords: Treatment, relapsed/refractory, Multiple myeloma, Clinical trial

Many patients with pulmonary arterial hypertension (PAH) experience substantial delays in diagnosis, which is associated with worse outcomes and higher costs. Tools for diagnosing PAH sooner may lead to earlier treatment, which may delay disease progression and adverse outcomes including hospitalization and death. We developed a machine-learning (ML) algorithm to identify patients at risk for PAH earlier in their symptom journey and distinguish them from patients with similar early symptoms not at risk for developing PAH. Our supervised ML model analyzed retrospective, de-identified data from the US-based Optum® Clinformatics® Data Mart claims database (January 2015 to December 2019). Propensity score matched PAH and non-PAH (control) cohorts were established based on observed differences. Random forest models were used to classify patients as PAH or non-PAH at diagnosis and at 6 months prediagnosis. The PAH and non-PAH cohorts included 1339 and 4222 patients, respectively. At 6 months prediagnosis, the model performed well in distinguishing PAH and non-PAH patients, with area under the curve of the receiver operating characteristic of 0.84, recall (sensitivity) of 0.73, and precision of 0.50. Key features distinguishing PAH from non-PAH cohorts were a longer time between first symptom and the prediagnosis model date (i.e., 6 months before diagnosis); more diagnostic and prescription claims, circulatory claims, and imaging procedures, leading to higher overall healthcare resource utilization; and more hospitalizations. Our model distinguishes between patients with and without PAH at 6 months before diagnosis and illustrates the feasibility of using routine claims data to identify patients at a population level who might benefit from PAH-specific screening and/or earlier specialist referral.