Ixazomib, daratumumab and low-dose dexamethasone in intermediate-fit patients with newly diagnosed multiple myeloma:an open-label phase 2 trial

Background: The outcome of non-transplant eligible newly diagnosed multiple myeloma (NDMM) patients is heterogeneous, partly depending on frailty level. The aim of this study was to prospectively investigate the efficacy and safety of Ixazomib-Daratumumab-low-dose dexamethasone (Ixa-Dara-dex) in NDMM intermediate-fit patients. Methods: In this phase II multicenter HOVON-143 study, IMWG Frailty index based intermediate-fit patients, were treated with 9 induction cycles of Ixa-Dara-dex, followed by maintenance with ID for a maximum of 2 years. The primary endpoint was overall response rate on induction treatment. Patients were included from October 2017 until May 2019. Trial Registration Number: NTR6297. Findings: Sixty-five patients were included. Induction therapy resulted in an overall response rate of 71%. Early mortality was 1.5%. At a median follow-up of 41.0 months, median progression-free survival (PFS) was 18.2 months and 3-year overall survival 83%. Discontinuation of therapy occurred in 77% of patients, 49% due to progression, 9% due to toxicity, 8% due to incompliance, 3% due to sudden death and 8% due to other reasons. Dose modifications of ixazomib were required frequently (37% and 53% of patients during induction and maintenance, respectively), mainly due to, often low grade, polyneuropathy. During maintenance 23% of patients received daratumumab alone. Global quality of life (QoL) improved significantly and was clinically relevant, which persisted during maintenance treatment. Interpretation: Ixazomib-Daratumumab-low-dose dexamethasone as first line treatment in intermediate-fit NDMM patients is safe and improves global QoL. However, efficacy was limited, partly explained by ixazomib-induced toxicity, hampering long term tolerability of this 3-drug regimen. This highlights the need for more efficacious and tolerable regimens improving the outcome in vulnerable intermediate-fit patients. Funding: Janssen Pharmaceuticals, Takeda Pharmaceutical Company Limited.</p

Linear interaction energy based prediction of cytochrome P450 1A2 binding affinities with reliability estimation.

Prediction of human Cytochrome P450 (CYP) binding affinities of small ligands, i.e., substrates and inhibitors, represents an important task for predicting drug-drug interactions. A quantitative assessment of the ligand binding affinity towards different CYPs can provide an estimate of inhibitory activity or an indication of isoforms prone to interact with the substrate of inhibitors. However, the accuracy of global quantitative models for CYP substrate binding or inhibition based on traditional molecular descriptors can be limited, because of the lack of information on the structure and flexibility of the catalytic site of CYPs. Here we describe the application of a method that combines protein-ligand docking, Molecular Dynamics (MD) simulations and Linear Interaction Energy (LIE) theory, to allow for quantitative CYP affinity prediction. Using this combined approach, a LIE model for human CYP 1A2 was developed and evaluated, based on a structurally diverse dataset for which the estimated experimental uncertainty was 3.3 kJ mol-1. For the computed CYP 1A2 binding affinities, the model showed a root mean square error (RMSE) of 4.1 kJ mol-1 and a standard error in prediction (SDEP) in cross-validation of 4.3 kJ mol-1. A novel approach that includes information on both structural ligand description and protein-ligand interaction was developed for estimating the reliability of predictions, and was able to identify compounds from an external test set with a SDEP for the predicted affinities of 4.6 kJ mol-1 (corresponding to 0.8 pKi units).The work was supported by Innovative Medicines Initiative Joint Undertaking (IMI-JU) under grant agreement no. 115002 (eTOX), resources of which are composed of financial contribution from the European Union Seventh Framework Programme/n(FP7/20072013) and EFPIA companies in kind contribution; www.etoxproject.eu. The work was also supported by The Netherlands Organisation for Scientific Research (NWO, VIDI grant 723.012.105); www.nwo.nl

Comparison between predicted inhibition mechanisms and experimentally determined inhibition mechanisms reported in literature.

<p>Comparison between predicted inhibition mechanisms and experimentally determined inhibition mechanisms reported in literature.</p

Prediction errors obtained for the external test set compounds.

<p>The compounds were grouped in a category according to the number of occurrences in which they were found to be an outlier according to analyses (A)-(D) in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0142232#pone.0142232.t004" target="_blank">Table 4</a>. Horizontal lines represent the standard error (SDEP) for a given category, while the boxes represent the standard deviation around this average.</p

Calculated (Δ<i>G_bind^Calc</i>) and observed (Δ<i>G_bind^Obs</i>) free energies of binding, and corresponding residuals (Δ<i>G_bind^Obs</i>—Δ<i>G_bind^Calc</i>) for the training-set compounds (kJ mol^-1).

<p>Calculated (Δ<i>G_bind^Calc</i>) and observed (Δ<i>G_bind^Obs</i>) free energies of binding, and corresponding residuals (Δ<i>G_bind^Obs</i>—Δ<i>G_bind^Calc</i>) for the training-set compounds (kJ mol^-1).</p

Calculated (Δ<i>G</i>_bind^Calc) and observed (Δ<i>G_bind^Obs</i>) free energies of binding (kJ mol^-1), and residuals (Δ<i>G_bind^Obs</i>–Δ<i>G</i>_bind^Calc) for the test-set compounds.

<p>Results from the reliability analyses are given as well, where a score 1 in columns (A)-(D) refers to the identification of outliers according to the following analyses: (A) Chemical similarity analysis; (B) Average interaction energy distribution analysis; (C) Ligand-residue electrostatic interaction analysis; (D) Ligand-residue van der Waals interaction analysis. In the last column (Total), the total sum of the number of analyses is reported in which a compound is identified as an outlier.</p

Per-residue decomposition analysis of the van der Waals interaction energies between the ligand and its surrounding in the protein-ligand simulations.

<p>(A) PCA loading plot for training-set van der Waals interaction energies; (B) Active site of CYP 1A2 from the crystallographic structure; heme group (purple carbon atoms), the co-crystallized ligand α-naphthoflavone (yellow carbon atoms), and amino acids with high loadings in the PCA are explicitly represented. Residues with high positive loadings on the first PC are depicted in green; Residues with high loadings on the second component are also represented, both for positive (blue) and negative values (red). (C) PCA score plot for the training-set (black circles) and test-set (white squares) compounds for the first two PCs. (D) Orthogonal distance (OD) of the compounds of the training set (black circles) and test set (white squares) from the model with 4 PCs. The dashed horizontal line represents the critical orthogonal distance, calculated for the training-set distribution.</p

Similarity matrix of the data set.

<p>Heat map of the compounds included in the training and test set, colored according to percent similarity expressed in terms of Tanimoto scores (TSs) between pairs of structural fingerprints (white = 100% similarity (TS = 1.00); black = 0% similarity (TS = 0.00)).</p

Per-residue decomposition analysis of the electrostatic interaction energies between the ligand and its surrounding in the protein-ligand simulations.

<p>(A) PCA loading plot for training-set electrostatic interaction energies; (B) Active site of CYP 1A2 from the crystallographic structure; heme group (purple carbon atoms), co-crystallized ligand α-naphthoflavone (yellow carbon atoms), and amino acids with high loading on the first two PCs (in red) are explicitly represented. (C) PCA score plot for the training-set (black circles) and test-set (white squares) compounds for the first two PCs. (D) Orthogonal distance (OD) of the compounds of the training set (black circles) and test set (white squares) from the model with 2 PCs. The dashed horizontal line represents the critical orthogonal distance, calculated for the training-set distribution.</p