Leveraging a Diabetes QSP Model to Drive Decisions in Target Identification and
Validation for Proinsulin to Insulin Conversion Therapy
Maria Trujillo PhD
Principal Scientist, Merck and Co Inc, Kenilworth, NJ
July 18, 2019 12:00-1:00 PM EDT
Registration (Free) at
https://register.gotowebinar.com/register/4089794427217565195?source=website
Abstract: Proinsulin is a precursor to insulin that is co-secreted into the
blood by the beta cell as a result of incomplete processing. Circulating
proinsulin levels increase with increasing insulin resistance in type 2
diabetes mellitus (T2DM). Unlike insulin, proinsulin has limited activity on
the insulin receptor. To assess whether the development of peptides engineered
to convert proinsulin to insulin in the blood would provide therapeutic value
in T2DM, we leveraged a diabetes quantitative systems pharmacology (QSP) model
(a physiologically based computational model of glucose homeostasis in humans);
internal clinical datasets, and external data from the literature.
In silico hypothesis testing included 1) the addition and qualification of
proinsulin biology into our diabetes QSP model, 2) the creation of virtual
patients (VP) to determine whether proinsulin conversion therapy may provide
value to a subpopulation of patients with T2DM based on phenotypic traits,
either as a monotherapy or in addition to standards of care (sulfonylureas and
metformin), and 3) the simulation of a phase 3 clinical trial with relevant
endpoints (including HbA1c and glucose, insulin, and proinsulin) and additional
mechanistic readouts (changes in circulating hormones and metabolites during
meals and glucose tolerance tests) to interrogate and interpret results.
As monotherapy, proinsulin conversion to insulin led to a ~0.2% reduction in
HbA1C in diabetic VPs with lesser effects (~0.1%) when added to a standard of
care. Virtual patients with higher proinsulin: insulin ratios at baseline
showed the greatest reductions. However, to achieve a clinically meaningful
HbA1C reduction of ≥ 0.5%, most VPs needed ratios above the reported
physiological range. The minimal influence of proinsulin conversion could be
explained by the proinsulin secretion and degradation rates relative to
respective rates for insulin; these system dynamics were a key learning from
the QSP modeling effort.
The lack of projected impact on HbA1C through conversion of proinsulin to
insulin was not intuitive prior to the in silico hypothesis testing using QSP
approaches. The simulation results were examined and challenged with rigor both
quantitatively and qualitatively and led to a recommendation not to pursue
proinsulin conversion as a potential T2DM therapy. The QSP modeling approach
was chosen to capture not only the dynamic interplay between proinsulin and
insulin kinetics but their impact on a complex multi-organ system that
maintains glucose homeostasis in the body. By thoroughly evaluating the
putative therapeutic in diabetic VPs in a Phase 3 setting, we were able to
generate sufficient scientific rationale for the termination decision. This
effort demonstrates how in silico hypothesis testing through QSP modeling may
aid in target identification and validation efforts in the discovery space,
conserving R&D resources for targets with greater probability of clinical
success.