Live in production · 7 sample reports below

Pre-screening PK reports
for biotech & CROs.

NCA, Bioequivalence, Food-Effect and Dose-Proportionality reports — the standard regulatory PK analyses, generated automatically from a CSV. Plus three advanced engines (Population PK, Tumor Growth, Drug Release) for cases where classical exponential models miss the picture. Non-regulated reports, automatic verdict, PDF in your inbox.

Free trial run. NCA, Tumor Growth and Drug Release are open now: upload your CSV directly, no signup — see pricing for the full Pre-PopPK go/no-go report. Reports are indicative (not for regulatory submission) and your uploaded data and report are deleted within 1 hour. Population PK is available on request. The NCA engine is validated against PKNCA 0.12.1 on the public reference theophylline dataset — see Algorithm Validation. The seven sample reports below use synthetic demonstration datasets, labelled as such in every PDF.

Live sample · NCA report
FractaLPK Engine
Observed concentration
Population mean ± SD
Time →
Concentration ↑
NCA — Population summary
Cmax, AUC0-t, AUC0-∞, λz, t½
Validated
PKNCA-aligned Quality flags per subject Regulatory disclaimer
Validated against PKNCA 0.12.1 on the reference theophylline dataset — agreement within 0.15 % on AUC-class parameters, bit-identical Cmax/Tmax.
What a report actually does
From the NCA showcase (24 synthetic subjects, declared units). The selling point isn't the plot — it's that the report refuses to report a λz it cannot defend, names the excluded subject, and gives the reason.
Subjects without a reportable λz — full traceability
Subjects without a reportable lambda-z: each excluded subject listed with adjusted R-squared and the reason for exclusion

This is what Phoenix does not do out of the box: when the terminal slope fails the reportability criteria (adjusted R² ≥ 0.90 over ≥ 3 points), the λz-derived parameters come back N/A with a named reason — not a confident-looking number the data don't support.

Concentration-time profiles on a semi-logarithmic axis
Concentration–time (semi-log), individual profiles + population mean.
Per-subject terminal-phase lambda-z regression diagnostic panels
Per-subject λz regression diagnostics — the points used, the fit, the R².
How it works
Open engines take a free trial run directly; request access for Population PK or proprietary-compound work.
Step 1
Explore the samples

Seven engines, seven synthetic demonstration datasets (labelled as such in every PDF). Open each one to see the verdict, models compared and diagnostic plots — exactly the report a paying client receives.

Step 2
Request access

The open engines (NCA, Tumor, Drug Release) need no request — take a free trial run directly. For Population PK or proprietary-compound work, tell us your name, company and what you're analysing; we reply within one business day with an upload link.

Step 3
Upload & receive

Upload your CSV, get a magic-link to the PDF when the fit completes (typically 5–10 minutes), and a copy in your inbox. No subscription, no commitment.

Live samples — open the PDFs
Each PDF is generated by the same engine that processes your uploads — same plots, same verdict logic, same honest exclusions and named non-reportable subjects.
The first four are the standard regulatory PK reports. The last three are the advanced fractional engines — see Advanced engines below.
Algorithm Validation
The NCA engine is cross-validated against PKNCA, the de-facto open-source standard for non-compartmental analysis.
NCA engine validated against PKNCA 0.12.1 on the public reference theophylline dataset (12 subjects, single oral dose). Agreement is within 0.15 % on AUC-class parameters and bit-identical on Cmax and Tmax across every subject. Pinned versions, full method-alignment table and per-subject diff are in the report.
Scope: this validation covers the NCA engine specifically. Bioequivalence wraps the same NCA core. Food-Effect and Dose-Proportionality are exploratory — not yet validated against reference software. The advanced fractional engines (PopPK, Tumor, Drug Release) are non-regulated screening tools and are not subject to this cross-validation.
Open Algorithm Validation report (PDF)
Fractional population route — research cross-check (study D1). Research cross-check (not part of any client report's fit): the same estimation engine fitted a fractional 1-CMT population model to diazepam (12 subjects) and reproduced the authors' published NONMEM output (Kaikousidis & Dokoumetzidis 2023, PMXathens/FDE4NONMEM): α 0.593 ± 0.011 vs 0.584 ± 0.011, within 2 SE. Pre-registered study D1, 406b63e.
Expert note: table 3 of the article reports α 0.54 ± 0.017, and our value falls outside 2 SE of that figure — as does the authors' own repository NONMEM output (0.584). The discrepancy is between the article and its repository and is not resolved here. The table 3 figure was read from the PMC abstract.
What makes FractaLPK different
Built for early-stage screening, when you need a fast answer before opening a full regulatory analysis.
Fractional models

Screen for memory effects / long tails against classical models. Validated behaviour (rich sampling ~14 points, ≤15% noise): the engine reliably recovers 1-compartment (classical/fractional) and 2-compartment classical structures, and reports statistical indistinguishability rather than crowning a fractional order when the data do not separate them — the common case. A screen, not a fractional-kinetics detector.

Multi-model verdict

Up to 8 candidate models fitted in parallel. The engine ranks them and applies an explicit decision rule — no manual model-selection bias.

Automatic diagnostics

Stability check, residuals and "Best fit" badge in every report. If no model wins clearly, the PDF says so — no false-positive verdicts.

Built for screening

Non-regulated, designed to sit before NONMEM / Monolix / PKanalix. Good for biotech early-stage, CROs running internal triage, and academic exploration.

Why not just use R + PsN?
R + PsN gives you the numbers. It does not give you a deliverable that declares its own limits, marks its own non-reportable subjects, and refuses to compute what it cannot defend. That deliverable is the product.
Marks its own non-reportable subjects

When a subject's terminal slope fails the reportability criteria, its λz-derived parameters come back N/A — listed by name, with the reason, in a "Subjects without a reportable λz" section. bbr / pmtables give you tables; they don't decide what should not be reported.

Refuses to compute what it can't defend

No CL/F when you haven't declared the dose units. No [P5, P95] at n < 5. No CTD node it cannot assign. Numbers appear at the precision the data support, and not otherwise — epistemic honesty is the point, not a footnote.

Zero R, zero NONMEM

Upload a CSV, receive a regulatory-format PDF + editable Word skeleton. The R stack assumes you already have a run and know how to write Quarto templates, pmtables headers and Xpose plotting code.

Minutes, not days of setup

CSV upload to PDF in your inbox: 5–10 min. Standing up an R / Quarto / PsN pipeline that produces the same self-documenting artefact takes a senior pharmacometrician days to weeks.

When FractaLPK is NOT for you: if you have an in-house pharmacometrics team already running PsN / bbr / pmtables, FractaLPK does not replace that workflow — your team's R pipeline is the right tool. FractaLPK is built for biotechs, CROs and academic groups that need a fast PK answer before opening a full regulatory analysis.
Beyond classical models — advanced fractional engines
The standard NCA / BE / FE / DP reports cover the regulatory baseline. The three advanced engines below are for cases where classical exponential ODEs visibly miss the data.
Population PK

Multi-compartment + fractional / Mittag-Leffler PK fits with explicit model-selection rule. For profiles where the terminal phase is heavier than a sum of exponentials predicts.

Open PopPK sample →
Drug Release

Higuchi / Korsmeyer-Peppas / Weibull / Mittag-Leffler stretched dissolution fits. Detects long-tail release driven by fractional memory that classical first-order under-models.

Open Drug Release sample →
Tumor Growth

Per-animal NLME (nlmixr2 FOCEI) over 6 classical structural models (Exponential / Logistic / Gompertz / Hahnfeldt classical / power-law / exponential-linear); fractional model not reported, selection criterion under review. Real %RSE, OMEGA matrix, η-shrinkage and CWRES — not naive pooled.

Open Tumor sample →

Model-risk report aligned with the vocabulary of ICH M15, with identifiability and sampling adequacy measured in pre-registered studies.

Of the 11 structure pairs we tested, 3 can be answered from a single concentration profile and 3 cannot be answered even with 50 subjects. For the remaining 5 we say exactly what is missing rather than give a number. The full table, with every figure traceable, is on the methodology page. Sampling needed before any fractional claim. A pre-registered study (3 360 simulated profiles, Sep 2026) measured what a design must have for each question: one disposition phase or two — 8 sampling points with the last one at 24 h; classical or fractional in a single phase — 8 points with the last one at 48 h, or 12 points to 24 h; classical or fractional in two phases — 24 points with the last one at 168 h, that is seven days. 8 of the 11 structure pairs did not separate under any design tested, up to 32 points and a 168 h window. The report states which of these your profile meets before it fits anything.
These three engines are research / screening tools; they are NOT cross-validated against PKNCA or any other regulatory reference. The "Best fit" verdict is FractaLPK's own decision rule; it frequently reports statistical indistinguishability rather than a fractional winner, because fractional order is often not identifiable from routine sampling. Designed for early triage, not regulatory submission.
Pricing
What a pharmacometrician decides by hand before opening NONMEM, delivered in minutes with every number traceable.
499 € per analysis · 299 € per month.
Billing is not live yet: trial runs on the open engines are free while we set it up.
10 analyses a month
299 € · per month
With history
  • 10 analyses a month, with history
  • Everything in the per-analysis plan