Frequently Asked Questions
Frequently Asked Questions
To make this clearer
“Biotech” is the industrial umbrella; “biotechnologies” are the underlying set of techniques and methods.
Fill in this form with abstract, TRL, IP and needs: we respond with an initial fit check.
Health & Safety (Health Biotech)
It is an approach that defines the the actual biology of the disease (tissues, markers and omics profiles where indicated) and links it to diagnostic and therapeutic decisions diagnostic and therapeutic personalised. It does not stop at generic classifications or guidelines applied wholesale: it builds a individual pathway measurable through robust clinical endpoints.
We start from risk classification and by the clinical scope of use. Requirements, risk management, clinical evaluation, software validation where present, and a plan for post-market surveillance. Documentation accompanies the entire lifecycle; compliance is not an event, it is a process.
It means speaking the standard languages of the sector (e.g. HL7/FHIR), maintain stable mappings, guarantee import/export without lock-in, and allow clinicians to see the data within its context (EHR, LIS, PACS) without duplication or manual copying.
Data-driven platforms collect, normalise and interpret medical data from certified devices, turning raw signals into clinically useful information for prevention, diagnosis and continuous monitoring.
Certified medical devices such as portable ECGs, pulse oximeters, multisensor wearables and environmental sensors underpin reliable remote monitoring and proactive prevention, both in healthcare and in the workplace.
Informed consent specific and understandable, compliant data governance, measures for pseudonymisation/de-identification, safety plans and audit documented, controls on the pre-analytical and analytical phases, and a return-of-results plan of results where required. If the project leads to clinical decisions or regulated products, additional requirements apply (clinical evaluations, MDR, GCP), and must be integrated from the outset.
An enabling role. The biobanks ensure traceable and comparable samples; the omics broaden the understanding of the underlying mechanisms (inflammatory, metabolic and genetic). When integrated with rigorous protocols and real-world data, make reproducible the evidence and accelerate translation towards clinical trials or clinical adoption.
With privacy by design and security by design: data minimisation, encryption in transit and at rest, access control, environment segmentation, audit trails and incident-response procedures. Roles and legal bases are made explicit; suppliers are also assessed for business continuity.
Integrated platforms enable intelligent surveillance, anticipating clinical events through composite indices and predictive algorithms and facilitating timely, personalised interventions instead of fixed-interval checks.
The data collected undergo normalisation, quality verification and audit-trail processes; they are managed according to security and privacy protocols compliant with applicable regulations (such as the GDPR).
With a reasoned request that describes purposes, study design, ethical criteria and sample/data requirements. The competent committee assesses consistency, proportionality and impact. Access, when granted, is conditional with quality protocols, traceability of use and the return of outcomes needed to update the biobank’s knowledge base.
Biopharmaceuticals & Therapies
It is thecompassionate use of an investigational medicinal product outside clinical trials, in patients with no valid therapeutic alternatives. It is governed by specific rules and does not replace trials: it addresses exceptional situations, with risk-benefit assessment and complete traceability.
The Phase 1 assesses safety and the dose-response profile (often in healthy volunteers or small groups of patients). The Phase 2 look for confirmation of effectiveness in selected populations, defines dose and regimen and prepares endpoints for the next phase. The Phase 3 verify definitively efficacy and safety on larger samples and with clinical endpoints shared with regulators
The Orphan Drug status applies to therapies for rare diseases with an unmet clinical need. It may include incentives through market exclusivity, reduced fees and dedicated regulatory support: it shortens timelines and makes development sustainable where the numbers would otherwise be discouraging.
We start from natural history and by the clinical need. If a surrogate is well linked to the mechanism, it can anticipate benefit; otherwise, you need functional outcomes and PRO validated. The endpoint hierarchy must be established in advance, with timelines compatible with disease progression.
They can be when randomisation is impractical or ethically questionable. But the control registry must have quality: comparable criteria, prospective or well-documented data collection, matching transparent. It is nevertheless a choice to discuss early with the regulator.
They are often part of theregulatory commitment in rare diseases: they consolidate evidence on long-term efficacy and safety, measure resource use and quality of life, and can inform HTA and payers in reimbursement reviews.
We start from modular structure (clinical, CMC, safety) and a narrative is built that links mechanism, data and clinical need. Every claim must be supported by verifiable documents: study reports, SOPs, statistical plans, CMC validations and pharmacovigilance plans. Before filing, we look for scientific advice/Type B meetings to align critical choices.
It depends on the therapeutic area and pathway. Between preclinical development and Phase 3, we generally speak of years; regulatory review requires months, less so through accelerated pathways. The real variable is the completeness and consistency of the dossier: what is missing today costs time tomorrow.
Integrity, traceability, consistency with the GCP. In practice: clear protocols, centre training, proportionate monitoring, rigorous management of missing data and deviations, pre-registered analyses, and robust safety oversight. “Good-looking” data that lack integrity are not data.
GREEN & AGRI-BIOTECHNOLOGY
It depends on the raw material and objective, but reductions are typically observed in energy consumption and emissions per unit of product, lower yield losses and a waste valorisation that reduces waste and disposal costs. The difference is made by the process control and the stability: without these two factors, the benefits evaporate in downtime and off-spec.
Start with the system boundaries (what I include and what I do not), choose baseline realistic and documents every assumption. Integrate LCA into the design process, not as a final audit: it will help you decide on suppliers, packaging, energy layout, logistics. If a choice worsens the key indicator, you discover it while there is still time to correct it, not after you have already invested.
Process bioengineering and quality with a by design. Without these two elements, data are of little use and sustainability remains a rhetorical exercise.
They are useful when they explain because a profile changes and as bring it back into the operating window. If they do not lead to a process decision, they are expensive noise.
With simple, durable KPIs: yield, stability (variance), off-spec per batch, energy per unit of product, CO₂/functional unit, mean time between corrective interventions. If these figures do not improve, they are not yet making an impact.
The environmental biotech applies biotechnology tools—microorganisms, microbial consortia, enzymes, plants—to remediation of contaminated sites and to monitoring soil, water and air. Unlike agri-food or pharmaceutical biotech, the objective is not to obtain a product, but to reduce pollutants and environmental impacts stably, measurably and scalably. Here the heart of the project is not only the biocatalyst, but the data platform that governs sensors, sampling, data quality and performance indicators.
In a in situ biotech bioremediation, selected microbial consortia degrade pollutants directly in the contaminated matrix (soil, groundwater, sediments). The project includes:
characterisation of the site and contaminants;
choice between biostimulation (optimise the environment for indigenous microorganisms) and bioaugmentation (add specialised strains or consortia);
control of key parameters (oxygen, nutrients, pH, moisture, temperature, inhibitors);
sensor network and periodic sampling, integrated into a data platform that makes it possible to understand where bioremediation works, where it slows down and when corrective action is needed.
Biosensors and data platforms transform environmental biotech from an “experiment” into continuous-monitoring infrastructure. The environmental biosensors (electrochemical and optical sensors, specific probes, qPCR, metabarcoding) measure physicochemical and biological parameters in near real time. The data platform collects, validates and synchronises data, applies quality controls, calculates composite indices and feeds predictive models. The result is a clear view for technicians, public bodies and stakeholders of: site health, bioremediation effectiveness, critical areas and distance from compliance targets.
The biotech bioremediation is particularly advantageous when the aim is to reduce large-scale excavation, waste transport and energy-intensive treatments. Integrated with LCA (Life Cycle Assessment) and plans for circular economy, makes it possible to demonstrate that the intervention reduces overall impacts rather than shifting them elsewhere: fewer lorries, lower emissions, a smaller water footprint, intelligent management of biological residues. For companies and public administrations, this means shorter remediation times, lower non-conformity risks, improved social acceptability and traceability of results based on data, not merely declarations.
Industry & Technology Transfer
With three moves:
1) FTO rigorous to avoid surprises,
2) claim built around non-obvious elements and foreseeable variants,
3) integration of IP within processes (CMC, contracts, suppliers).
Monetisation becomes visible in the numbers when IP reduces copying risk, increases negotiating power and enables Patent Box measurable
Start with what you cannot afford to get wrong: plants and environments (capex) that guarantee quality and repeatability; people and controls (opex) that keep the process on course. Size capex according to DoE and robustness studies to avoid buying a plant that the biology cannot sustain; plan opex with clear KPIs (yield, off-spec, batch-release time, energy/unit) to avoid “paper” budgets.
Start with the critical materials and from the high-risk phases: define specifications that genuinely measure what you need (not “out of habit”), select and qualification suppliers through targeted audits, implement a dual-sourcing real, logistics map and cold chain with data, not hypotheses. Integrate quality and procurement: if procurement changes a supplier solely on price without understanding the effects on the process, you are buying deviations in the catalogue.
It means that you decide before how quality will arise from the process: identify which product attributes matter (CQA), which process parameters govern them (CPP), which range keep everything within a project space robust. You implement PAT and in-process controls to detect drift and projects comparability and plans for validation consistent across changes in scale, site or equipment. Quality is not the release test: it is the process capability to repeat consistently.
A biotech IP strategy when well constructed, facilitates access to instruments such as Patent Box and other tax incentives linked to intellectual property. This requires clear rights (patents, software, protected know-how), traceability of R&D costs linked to the asset, and technical documentation demonstrating the relationship between innovation, economic outcomes and tax benefit. The typical mistake is to think about Patent Box only downstream: in reality, it is better to design IP and the technical-financial pathway in a coordinated way from the outset.
In due diligence on biotech IP, investors and partners look not only at the number of patents but at the quality of the IP position: strength of claims against prior art, freedom to operate (FTO), clarity of ownership and co-ownership, absence of hidden constraints from legacy agreements, and consistency among IP, TRL and the regulatory plan. Governance also matters: who decides on extensions, licensing and enforcement, and how conflicts of interest among academia, spin-offs and industry are managed. An IP strategy that is easy to explain and well documented carries as much weight as a strong experimental result.
The trajectory is reconstructed TRL, we assess the data quality (clinical/CMC/device/software), we estimate timelines and costs of the next steps, the blocking risks and we define milestone that reduce them. Only then do we discuss equity, royalties or fees.
We separate Background and Foreground IP, we establish fields of use and territories, rights are defined for sublicence and reversion clauses. It is established who files, who pays and who collects, with clear pre-publication review processes and practical measures to protect know-how and secrets.
It means verifying integrity, traceability, semantic and technical standards, access governance, legal basis, security and usability of datasets for the purposes of the partnership. Without this verification, the agreement stalls when decisions have to be made.
In the biotech the development cycle is long, costly and regulated: without a strategy for intellectual property (IP) clear, it is difficult to convince investors, industrial partners and regulators that the project will generate defensible value. Strong IP makes it possible to protect molecules, processes, platforms or algorithms, structure licensing and co-development agreements, and access instruments such as Patent Box and subsidised finance. In practice, IP is the legal translation of years of R&D into a negotiable asset.
There is no single answer: in many cases, an effective biotech IP strategy combines patent and trade secret. A patent makes sense when a strong, publicly recognised right is needed to engage with investors, partners and regulators; a trade secret is preferable when the know-how is difficult to reverse engineer or when disclosure through a patent would expose too much of the technology. The choice depends on TRL, the competitive landscape, the mode of industrial exploitation and the risks of know-how leakage.
For a biotech patent with a good chance of withstanding examination and due diligence, at minimum you need: robust experimental data (not merely a concept), a clear distinction from the prior art, a precise definition of the scope of protection (molecule, use, process, kit, platform), traceable documentation of who did what and when, and consistency between patent claims and the development plan (TRL, future studies, regulatory requirements). Without these elements, the risk is paying for a weak right that is difficult to enforce or monetise.
Careers & Skills
Yes, if it includes work on data and experimental project real, with measurable objectives and process responsibilities. Avoid purely theoretical pathways: look for programmes with partner laboratories, pilot plants, access to datasets and opportunities to publish reproducible pipelines.
Process bioengineers with QbD/PAT expertise, bioinformaticians with experience in omics and integration RWD, data scientists with clinical awareness and explainability, QA/QC with a culture of data integrity and GxP, regulatory specialists able to engage with CMC and clinical teams.
Take-home message artefacts: adopted SOPs, pipelines in use, curated datasets, pre-submission to EMA/FDA where you contributed. Describe impacts (shorter timelines, improved data quality, stable yields) and cite references that can confirm them.
On average, those with regulatory accountability (QA/RA/CMC) and those that affect margins/timelines (scale-up, supply chain, data/AI in the plant). BD/licensing roles also pay well when they deliver agreements.
Yes, if you can see liquidity events plausible or buyback programmes, and whether the fixed component allows you to maintain a sustainable standard of living. Ask about vesting, strike and clauses in the event of exit or change of control.
Yes, if you become reference on a critical axis (QbD, RA, regulated AI, CMC). Many companies have dual tracks: technical ladder with senior/principal levels and management ladder.
Search adherence between what you want to learn and what that network actually does every day. Question: will I return with a pipeline, a dataset, or a technique that we will actually use? If the answer is no, change network or redesign the objective.
Yes, but files first. Novelty is a binary switch: a public disclosure before filing turns it off. By coordinating timing and content, it is possible to protect and publish without losing value.
Just enough to complete a deliverable non-trivial (pipeline, dataset, protocol). Three months with an adopted outcome is better than a year “spent observing”.
When the company provides real problems, access to data/plants, a technical supervisor that exists, and when the project produces artefacts that remain (SOPs, recipes, models). If the industrial component is only a letter of intent, it is not an industrial doctorate.
Yes, if you return with asset (pipelines, SOPs, datasets, relationships) and choose organisations that can use them. Bring references and measurable cases; explain how those results translate into advantage for the new employer.