I was a co-author on this manuscript as I contributed to the bone marrow blast count analysis.

Link to the article: https://doi.org/10.1182/bloodadvances.2016001925

Citation: DeAngelo, Daniel J., et al. “Inotuzumab ozogamicin in adults with relapsed or refractory CD22-positive acute lymphoblastic leukemia: a phase 1/2 study.” Blood advances 1.15 (2017): 1167-1180.

Abstract:

This study evaluated the safety, antitumor activity, pharmacokinetics, and pharmacodynamics
of inotuzumab ozogamicin (InO) for CD22-positive relapsed/refractory acute lymphoblastic
leukemia. In phase 1, patients received InO 1.2 (n 5 3), 1.6 (n 5 12), or 1.8 (n 5 9) mg/m2
per cycle on days 1, 8, and 15 over a 28-day cycle (#6 cycles). The recommended phase 2 dose
(RP2D) was confirmed (expansion cohort; n 5 13); safety and activity of InO were assessed in
patients receiving the RP2D in phase 2 (n 5 35) and in all treated patients (n 5 72). The RP2D
was 1.8 mg/m2 per cycle …

The QTc modeling we performed for glasdegib was published in the Expert Review of Clinical Pharmacology.

Link to this article: https://doi.org/10.1080/17512433.2021.1925538

Citation: Luke K. Fostvedt, Naveed Shaik, Giovanni Martinelli, Andrew J. Wagner
& Ana Ruiz-Garcia (2021) Exposure–response modeling of the effect of glasdegib on cardiac
repolarization in patients with cancer, Expert Review of Clinical Pharmacology, 14:7, 927-935, DOI:
10.1080/17512433.2021.1925538

ABSTRACT
Purpose: To characterize the effect of glasdegib on cardiac repolarization (QTc) in patients with
advanced cancer.
Methods: A concentration–QTc model was developed using data from two glasdegib single-agent,
dose-escalation trials. Triplicate electrocardiogram was performed at pre-specified timepoints paired
with pharmacokinetic blood collections after a single dose and at steady-state. Changes in QTc from
baseline were predicted by model-based simulations at the clinical dose (100 mg QD) and in
a supratherapeutic setting.
Results: Glasdegib did not affect the heart rate, but had a positive effect on the corrected QT …

The lorlatinib ADME studies were published in the Journal of Clinical Pharmacology.

Link to the manuscript: https://doi.org/10.1002/jcph.1621

Citation: Stypinski, D., Fostvedt, L., Lam, J.L., Vaz, A., Johnson, T.R., Boerma, J.S. and Pithavala, Y.K. (2020), Metabolism, Excretion, and Pharmacokinetics of Lorlatinib (PF-06463922) and Evaluation of the Impact of Radiolabel Position and Other Factors on Comparability of Data Across 2 ADME Studies. The Journal of Clinical Pharmacology, 60: 1254-1267. 

Abstract: While an initial clinical absorption, distribution, metabolism, and excretion (ADME) study (Study 1; N = 6) with 100 mg/100 µCi [¹⁴C]lorlatinib, radiolabeled on the carbonyl carbon, confirmed that the primary metabolic pathways for lorlatinib are oxidation (N-demethylation, N-oxidation) and N-glucuronidation, it also revealed an unanticipated, intramolecular cleavage metabolic pathway of lorlatinib, yielding a major circulating benzoic acid metabolite (M8), and an unlabeled pyrido-pyrazole substructure. Concerns regarding the fate of unknown metabolites associated with this intramolecular cleavage …

At the thirteenth annual American Conference on Pharmacometrics (ACoP13), I gave a presentation that was an introduction/overview of joint models of longitudinal and time to event data. The slides from the talk can be downloaded here: acop4b-overview

Since joint modeling is the basis of many models used for pharmacometric applications, the intent of these slides was to give some of the general concepts together with multiple references for someone to get started if they are interested in learning more about this class of joint models. Some of the content is taken from other presentations and I have tried to make sure the original source is cited in all cases.

The general structure of the slides is:

1. What is Joint Modeling of longitudinal and time to event data
2. How do we develop joint models.
3. Stepwise versus simultaneous estimation
4. Bayesian Joint Models
5. Applications of Joint Models with directions to stan code
6. Additional

Luke Fostvedt, Dan Nordman, Alyson Wilson

Abstract: We consider a model for generating a random graph using the configuration model. In the configuration model, each node draws a degree independently from a marginal degreed distribution and endpoints pair randomly. We establish non-trivial bounds on the expected sizes of “buckets ” for large graphs. We define nodes i and j in a graph as neighbors if they share an edge, and we define the “bucket ” associated with node i as the set of nodes that are its neighbors and have degree greater than node i. We formalize this argument by providing an analysis for the expected number of items and pairs in a “bucket ” for arbitrarily specified degree distributions, which include power laws.

Paper: Technical Report…