Regenerative Oncology

Steering Cellular
Destiny.

Moving oncology from a stochastic "cell-kill" model to a deterministic "state-steering" paradigm using vector-field topography.

The Paradigm Shift

Building Slides, Not Bombs.

The current standard of care for glioblastoma (GBM) is analogous to carpet-bombing the mountain. We are changing the physics of the treatment.

The Old Paradigm

Somatic Mutation Theory & Cytotoxicity. We've historically viewed cancer as uncontrollably dividing cells. Therapy aims for "cell-kill" (radiation, chemo). However, surviving cells exhibit lineage plasticity. They climb out of their differentiated valleys, escaping cytotoxic stress by adopting resistant, stem-like states.

  • Stochastic and highly toxic
  • Drives Proneural-to-Mesenchymal Transition (PMT)
  • High recurrence rates in GBM

The E.sapiens Approach

Epigenetic Landscapes & State-Steering. Cancer is cells climbing the Waddington landscape. We don't want to destroy the cell; we want to force it to mature. Differentiation therapy uses specific directional pushes to guide Glioblastoma Stem Cells (GSCs) back down into stable, post-mitotic valleys.

  • Deterministic and precision-targeted
  • Traps cells in terminal differentiation
  • Utilizes the body's native developmental programs
Our Research

Project RE-MAP

Reverse-Engineering Malignancy via Attractor Perturbation. Integrating scRNA-seq, RNA velocity, and massive perturbation databases.

STEP 01

Landscape Reconstruction

Using Single-Cell Multiomics (scRNA-seq + scATAC-seq) and Waddington-OT, we reconstruct the patient-specific epigenetic landscape from patient-derived organoids.

STEP 02

Escapement Trajectories

Deploying scVelo and Dynamo to calculate real-time RNA velocity. We map the continuous vector field to identify the precise paths cells take to become resistant (v_escape).

STEP 03

High-Throughput Validation

Top predicted 'Shifter' compounds are rigorously validated using automated, high-content imaging of 3D patient-derived organoids to confirm morphological maturation.

Proprietary Core

The VECTR-Match
Computational Engine.

VECTR-Match bridges the gap between static drug signatures and dynamic cellular fate. It screens the LINCS L1000 database (>1.3M profiles) not for cytotoxicity, but for mathematical force-field alignment.

// The Optimization Function
Maximize drug alignment score:
ScoreP = (FP · vtarget) - λ * (FP · vescape)
  • State Embedding (scGen) Projects high-dimensional L1000 signatures into the latent space of the specific GBM atlas using variational autoencoders.
  • Vector Dot Product Calculates the alignment between the drug's perturbation vector and the tumor's natural differentiation vector.
X Y 1 2 3 4 5 1 2 3 4 5 Unperturbed Path (No Drug) Stem State (GSC) — Deepest v_escape (PMT) — Deep Terminal Lineage — Shallow F_drug Depth Tight = Deep Wide = Shallow
GSC
v_escape
Terminal
F_drug
Market & Traction

Scaling the Future of Oncology.

Glioblastoma represents a $3B+ annual market with zero significant survival improvements in 20 years. E.sapiens is built to disrupt this stagnation.

1.3M+
Perturbation Profiles
100x
Screening Efficiency
3D
Organoid Validation
IP
Proprietary Engine
Live Pipeline

Development Roadmap

PHASE 1: VECTR-Match v2.0

Integration of spatial transcriptomics for microenvironment-aware steering.

PHASE 2: Lead Optimization

In-vivo validation of top 3 'Shifter' candidates in orthotopic GBM models.

PHASE 3: IND-Enabling Studies

Safety profiling and clinical trial design for first-in-human differentiation therapy.

The E.sapiens Network

Scientific Advisory.

Our approach is guided by world-class experts in computational biology, oncology, and regenerative medicine.

SK

Shabab Khan

Founder & Lead Architect

Pioneering the application of vector-field topography to cellular state transitions.

Advisory Board

Top-tier Institutions

This could be you

Clinical Partners

Oncology Centers

This could be you

Get In Touch

Partner With E.sapiens.

Whether you're an investor, researcher, or clinician, we'd love to hear from you.